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final version for greens report

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Ruben van de Ven 2021-10-13 18:35:49 +02:00
parent c81ec7bf18
commit 8cb37534fc
7 changed files with 453 additions and 1132 deletions
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18
www/graph.css
View file

@ -1,9 +1,4 @@
@font-face {
font-family: 'Lexend Mega Regular';
font-style: normal;
font-weight: normal;
src: local('Lexend Mega Regular'), url('LexendMega-Regular.woff') format('woff');
}
:root {
--color1: #9741f9;
@ -38,13 +33,13 @@ body {
/* background: linear-gradient(to top, #040308, #AD4A28, #DD723C, #fc7001, #dcb697, #9ba5ae, #3e5879, #020b1a); */
background: var(--body-back);
/* background: #9cb3c9; */
font-family: sans-serif;
font-family: 'CelloSansRegular', sans-serif;
min-height: 100vh;
}
svg {
cursor: grab;
font-family: sans-serif;
font-family: 'CelloSansRegular', sans-serif;
}
svg.dragging {
@ -136,7 +131,7 @@ svg #header #titlePath, svg #header #title2Path, svg #header #subtitlePath {
svg #header text {
font-size: 180px;
font-family: "Lexend Mega Regular";
font-family: "CelloSansRegular", sans-serif;
/*Comfortaa*/
opacity: .8;
fill: var(--title-color);
@ -627,5 +622,8 @@ p.subtitle {
}
#sources a{
color: lightblue;
color: var(--link-hover-related-color);
}
#sources a:hover{
color: var(--link-hover-color);
}

182
www/graph.js
View file

@ -114,6 +114,7 @@ const CONFIG = {
"Software Developer",
"Dataset Developer",
"Related Institutions",
"Is Department Of",
"Involved Entities",
],
@ -154,6 +155,10 @@ const CONFIG = {
"label": "is related to",
"swap": true,
},
"Is Department Of": {
"label": "is part of",
"swap": false,
},
"Involved Entities": {
"label": "is involved in",
"swap": true,
@ -363,6 +368,8 @@ function getLinkId(link) {
class NodeMap {
constructor(parent) {
this.ready = false;
this.readyCallback = false;
this.root = d3.select(parent);
this.resizeEvent = window.addEventListener('resize', this.resize.bind(this));
this.tooltipEl = document.getElementById('tooltip');
@ -542,57 +549,46 @@ class NodeMap {
}
});
this.title = this.container.append('g').attr('id', 'header');
// this.title = this.container.append('g').attr('id', 'header');
const titleFeature = {
"type": "LineString",
"coordinates": []
};
const title2Feature = {
"type": "LineString",
"coordinates": []
};
const subtitleFeature = {
"type": "LineString",
"coordinates": []
};
for (let index = 26; index < 70; index++) {
// projection apparently tries to find the shortest path between two points
// which is NOT following a lat/lon line on the globe
titleFeature.coordinates.push([index, 52]);
title2Feature.coordinates.push([index, 50.5]);
subtitleFeature.coordinates.push([index, 49]);
}
this.title.append("path")
.attr("id", "titlePath")
.attr("d", this.proj(titleFeature))
;
this.title.append("path")
.attr("id", "title2Path")
.attr("d", this.proj(title2Feature))
;
this.title.append("path")
.attr("id", "subtitlePath")
.attr("d", this.proj(subtitleFeature))
;
this.title.append("text")
.html('<textPath xlink:href="#titlePath">Remote Biometric</textPath>')
this.title.append("text")
.html('<textPath xlink:href="#title2Path">Identification</textPath>')
this.title.append("text")
.attr("id", "subtitle")
.html('<textPath xlink:href="#subtitlePath">' + CONFIG.subtitle + '</textPath>')
// this.title.append('text')
// .attr('class', 'title')
// .attr('x', 1000)
// .attr('y', 1000)
// .text(CONFIG.title);
// this.title.append('text')
// .attr('class', 'subtitle')
// .attr('x', 1000)
// .attr('y', 1200)
// .text(CONFIG.subtitle);
// const titleFeature = {
// "type": "LineString",
// "coordinates": []
// };
// const title2Feature = {
// "type": "LineString",
// "coordinates": []
// };
// const subtitleFeature = {
// "type": "LineString",
// "coordinates": []
// };
// for (let index = 26; index < 70; index++) {
// // projection apparently tries to find the shortest path between two points
// // which is NOT following a lat/lon line on the globe
// titleFeature.coordinates.push([index, 52]);
// title2Feature.coordinates.push([index, 50.5]);
// subtitleFeature.coordinates.push([index, 49]);
// }
// this.title.append("path")
// .attr("id", "titlePath")
// .attr("d", this.proj(titleFeature))
// ;
// this.title.append("path")
// .attr("id", "title2Path")
// .attr("d", this.proj(title2Feature))
// ;
// this.title.append("path")
// .attr("id", "subtitlePath")
// .attr("d", this.proj(subtitleFeature))
// ;
// this.title.append("text")
// .html('<textPath xlink:href="#titlePath">Remote Biometric</textPath>')
// this.title.append("text")
// .html('<textPath xlink:href="#title2Path">Identification</textPath>')
// this.title.append("text")
// .attr("id", "subtitle")
// .html('<textPath xlink:href="#subtitlePath">' + CONFIG.subtitle + '</textPath>')
this.link = this.container.append("g")
.attr('class', 'links')
@ -668,6 +664,34 @@ class NodeMap {
this.update();
setTimeout(() => this.calculateLabels(), 1000);
this.ready = true;
if(this.readyCallback)
this.readyCallback();
}
triggerReset(){
const cb = () => {
this.deselectNode();
this.resetZoom();
}
if(this.ready){
cb();
} else {
this.readyCallback = cb;
}
}
triggerSelect(toSelect){
const cb = () => {
const node = this.graph.nodes.filter(n => n.id == toSelect)[0]
this.selectNode(node);
}
if(this.ready){
cb();
} else {
this.readyCallback = cb;
}
}
resetZoom() {
@ -908,6 +932,39 @@ class NodeMap {
this.sourcesEl.classList.remove('visible');
}
hoverNode(evt, n){
console.log('hover!', n)
// d3.select(this).classed('hover', true);
const links = document.getElementsByClassName('link');
const linkedLinks = [];
for (let link of links) {
const l = d3.select(link).datum();
if (n == l.target || n == l.source) {
link.classList.add('linkedHover');
// make sure it's the last element, so it's drawn on top
// link.parentNode.appendChild(link); .. causes gliches
// find related related node:
const otherNode = n == l.target ? l.source : l.target;
const otherNodeEl = document.getElementById(otherNode.id);
otherNodeEl.classList.add('linkedHover');
linkedLinks.push(l);
}
}
if(evt){
this.showTooltip(evt.target, n, linkedLinks);
}
}
endHoverNode(n){
this.hideTooltip();
const links = document.getElementsByClassName('linkedHover');
while (links.length) {
links[0].classList.remove('linkedHover');
}
}
update() {
// console.log(this.graph)
@ -925,31 +982,10 @@ class NodeMap {
evt.stopPropagation(); this.selectNode(n);
});
group.on("mouseover", (evt, n) => {
// d3.select(this).classed('hover', true);
const links = document.getElementsByClassName('link');
const linkedLinks = [];
for (let link of links) {
const l = d3.select(link).datum();
if (n == l.target || n == l.source) {
link.classList.add('linkedHover');
// make sure it's the last element, so it's drawn on top
// link.parentNode.appendChild(link); .. causes gliches
// find related related node:
const otherNode = n == l.target ? l.source : l.target;
const otherNodeEl = document.getElementById(otherNode.id);
otherNodeEl.classList.add('linkedHover');
linkedLinks.push(l);
}
}
this.showTooltip(evt.target, n, linkedLinks);
this.hoverNode(evt, n);
});
group.on("mouseout", (evt, n) => {
this.hideTooltip();
const links = document.getElementsByClassName('linkedHover');
while (links.length) {
links[0].classList.remove('linkedHover');
}
this.endHoverNode(n);
});
// group.append('circle').attr("r", 5 /*this.nodeSize*/);
group.append('path')

7
www/index.html
View file

@ -2,8 +2,9 @@
<head>
<meta charset="utf-8">
<link rel="stylesheet" media="screen" href="https://fontlibrary.org//face/cello-sans" type="text/css"/>
<link rel="stylesheet" href="graph.css">
<title>Remote Biometric Identification | A survey of the European Union</title>
<title>Biometric and Behavioural Mass Surveillance in EU Member States</title>
</head>
<body>
@ -14,8 +15,8 @@
<div id='map'></div>
<header>
<h1>Remote Biometric Identification</h1>
<p class='subtitle'>A survey of the European Union</p>
<h1>Biometric and Behavioural Mass Surveillance</h1>
<p class='subtitle'>in EU Member States</p>
<aside id="filters">
<h3 onclick="this.parentNode.classList.toggle('hide');">Filter</h3>

46
www/report/report.css
View file

@ -3,8 +3,8 @@
:root {
--border-radius: 5px;
--box-shadow: 2px 2px 10px;
--color: blue;
--hover-color: lightblue;
--color: #d1bce9;
--hover-color: #9741f9;
--color-accent: #118bee15;
--color-bg: #fff;
--color-bg-secondary: #e9e9e9;
@ -13,7 +13,7 @@
--color-shadow: #f4f4f4;
--color-text: #000;
--color-text-secondary: #999;
--font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen-Sans, Ubuntu, Cantarell, "Helvetica Neue", sans-serif;
--font-family: 'CelloSansRegular', sans-serif;
--hover-brightness: 1.2;
--justify-important: center;
--justify-normal: left;
@ -28,7 +28,7 @@ iframe {
position: fixed;
top: 0;
left: 0;
width: calc(100% - var(--width-content) - 2rem);
width: calc(100% - var(--width-content) - 4rem);
height: 100vh;
border-style: none none none none;
/* border-width: 3px;
@ -87,8 +87,8 @@ header,
main {
margin: 0 0 0 auto;
max-width: var(--width-content);
width: calc(100% - 2rem);
padding: .5rem 1rem;
width: calc(100% - 4rem);
padding: .5rem 2rem;
}
header{
@ -100,7 +100,7 @@ header{
background-color: white;
border-bottom: solid 1px var(--color-bg-secondary);
z-index: 999;
max-width: calc(var(--width-content) + 2rem);
max-width: calc(var(--width-content) + 4rem);
}
@media screen and (max-width: 740px) {
@ -171,7 +171,7 @@ header a strong {
header nav > a{
color: var(--color-text);
padding-left: .5rem
/* padding-left: .5rem */
}
header nav img {
margin: 1rem 0;
@ -186,11 +186,14 @@ section header {
nav {
align-items: center;
display: flex;
font-weight: bold;
/* font-weight: bold; */
justify-content: space-between;
padding:0 2rem;
/* margin-bottom: 7rem; */
}
nav > a{ font-weight: bold;}
nav ul {
list-style: none;
padding: 0;
@ -364,21 +367,28 @@ sup::after{
} */
/* Links */
a {
color: var(--color);
display: inline-block;
font-weight: bold;
a, a sup {
color: inherit;
border-bottom: solid 2px var(--color);
display: inline;
/* font-weight: bold; */
text-decoration: none;
}
a:hover {
a:hover, a:hover sup {
color: var(--hover-color);
border-color: var(--hover-color);
/* text-decoration: underline; */
}
a:hover sup{
a.footnote-ref{
border:none;
/* background-color: var(--hover-color); */
}
nav a{
border: none;
}
a b,
a em,
a i,
@ -586,8 +596,8 @@ h1.Title{
.keypoints{
background-color: black;
color: var(--color-bg-secondary);
padding: 1rem;
margin: 0 -1rem;
padding: 2rem;
margin: 0 -2rem;
font-weight: bold;
}
.keypoints > p > strong{
@ -597,4 +607,4 @@ h1.Title{
a.maplink{
cursor: pointer;
}
}

517
www/report/report.html
View file

@ -5,6 +5,88 @@
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
<title>Biometric and Behavioural Mass Surveillance in EU Member States</title>
<script>
(function(document, history, location) {
var HISTORY_SUPPORT = !!(history && history.pushState);
var anchorScrolls = {
ANCHOR_REGEX: /^#[^ ]+$/,
OFFSET_HEIGHT_PX: 100,
/**
* Establish events, and fix initial scroll position if a hash is provided.
*/
init: function() {
this.scrollToCurrent();
window.addEventListener('hashchange', this.scrollToCurrent.bind(this));
document.body.addEventListener('click', this.delegateAnchors.bind(this));
},
/**
* Return the offset amount to deduct from the normal scroll position.
* Modify as appropriate to allow for dynamic calculations
*/
getFixedOffset: function() {
return this.OFFSET_HEIGHT_PX;
},
/**
* If the provided href is an anchor which resolves to an element on the
* page, scroll to it.
* @param {String} href
* @return {Boolean} - Was the href an anchor.
*/
scrollIfAnchor: function(href, pushToHistory) {
var match, rect, anchorOffset;
if(!this.ANCHOR_REGEX.test(href)) {
return false;
}
match = document.getElementById(href.slice(1));
if(match) {
rect = match.getBoundingClientRect();
anchorOffset = window.pageYOffset + rect.top - this.getFixedOffset();
window.scrollTo(window.pageXOffset, anchorOffset);
// Add the state to history as-per normal anchor links
if(HISTORY_SUPPORT && pushToHistory) {
history.pushState({}, document.title, location.pathname + href);
}
}
return !!match;
},
/**
* Attempt to scroll to the current location's hash.
*/
scrollToCurrent: function() {
this.scrollIfAnchor(window.location.hash);
},
/**
* If the click event's target was an anchor, fix the scroll position.
*/
delegateAnchors: function(e) {
var elem = e.target;
if(
elem.nodeName === 'A' &&
this.scrollIfAnchor(elem.getAttribute('href'), true)
) {
e.preventDefault();
}
}
};
window.addEventListener(
'DOMContentLoaded', anchorScrolls.init.bind(anchorScrolls)
);
})(window.document, window.history, window.location);
</script>
<link rel="stylesheet" media="screen" href="https://fontlibrary.org//face/cello-sans" type="text/css"/>
<link rel="stylesheet" href="report.css" />
<style type="text/css">
@ -38,11 +120,12 @@
// navItemEl.classList.remove('active');
} else {
if(toSelect === null) {
frameEl.contentWindow.mapGraph.deselectNode();
frameEl.contentWindow.mapGraph.resetZoom();
frameEl.contentWindow.mapGraph.triggerReset();
// frameEl.contentWindow.mapGraph.deselectNode();
// frameEl.contentWindow.mapGraph.resetZoom();
} else {
const node = frameEl.contentWindow.mapGraph.graph.nodes.filter(n => n.id == toSelect)[0]
frameEl.contentWindow.mapGraph.selectNode(node);
frameEl.contentWindow.mapGraph.triggerSelect(toSelect);
// frameEl.contentWindow.mapGraph.selectNode(node);
}
// navItemEl.classList.add('active');
}
@ -231,7 +314,7 @@
</tr>
<tr class="odd">
<th>BPOL</th>
<td>German Federal Police</td>
<td><a class="maplink" data-title="German Federal Police (Bundespolizei)">German Federal Police</a></td>
</tr>
<tr class="even">
<th><a class="maplink" data-title="CATCH">CATCH</a></th>
@ -535,7 +618,7 @@
<li><p>Private and public actors are increasingly deploying “smart surveillance” solutions including RBI technologies which, if left unchecked, could become biometric mass surveillance.</p></li>
<li><p>Facial recognition technology has been the most discussed of the RBI technologies. However, there seems to be little understanding of the ways in which this technology might be applied and the potential impact of such a broad range of applications on the fundamental rights of European citizens.</p></li>
<li><p>The development of RBI systems by authoritarian regimes which may subsequently be exported to and used within Europe is of concern. Not only as it pertains to the deployments of such technologies but also the lack of adequate insight into the privacy practices of the companies supplying the systems.</p></li>
<li><p>Four main positions have emerged among political actors with regard to the deployments of RBI technologies and their potential impact on fundamental rights: 1) active promotion 2) support with safeguards; 2) moratorium and 3) outright ban.</p></li>
<li><p>Four main positions have emerged among political actors with regard to the deployments of RBI technologies and their potential impact on fundamental rights: 1) active promotion 2) support with safeguards; 3) moratorium and 4) outright ban.</p></li>
</ul>
<p><strong>CHAPTER 2: Technical Overview</strong></p>
<ul>
@ -613,11 +696,11 @@
<li><p>The <a class="maplink" data-title="Dragonfly Project">Dragonfly Project</a> has elicited numerous warnings regarding data protection and the rights to privacy from both public and private organisations. However the lack of contestation and social debate around the issues of privacy and human rights in relation to such projects as the Hungarian Government’s Dragonfly is striking.</p></li>
</ul>
<p><strong>CHAPTER 11: Recommendations</strong></p>
<p><strong>1. The EU should prohibit the deployment of both indiscriminate and “targeted” Remote Biometric and Behavioural Identification technologies in public spaces, as it amounts to mass surveillance.</strong></p>
<p><strong>1. The EU should prohibit the deployment of both indiscriminate and “targeted” Remote Biometric and Behavioural Identification (RBI) technologies in public spaces (real-time RBI), as well as ex-post identification (or forensic RBI). Our analysis shows that both practices, even when used for “targeted surveillance” amount to mass surveillance.</strong></p>
<ul>
<li><p>The EU should <strong>prohibit the deployment of Remote Biometric and Behavioural Identification technologies in public spaces</strong>, in line with similar recommendations made by the EDPB and the EDPS.<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a></p></li>
<li><p>This concerns indiscriminate use of such technologies in public spaces and concerns not only the acquisition and processing of <strong>faces, but also gait, voice and other biometric or behavioural signals.</strong></p></li>
<li><p>It also concerns the use of such technologies for <strong>“targeted surveillance”</strong> as the practice might be considered as expansive and intrusive to an extent that it would constitute disproportionate interference with the rights to privacy and personal data protection.</p></li>
<li><p>In line with similar recommendations made by the EDPB and the EDPS,<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a> the EU should <strong>prohibit the deployment of Remote Biometric and Behavioural Identification technologies in public spaces</strong></p></li>
<li><p>In line with the position of the EDRi regarding’s EU Artificial Intelligence Act<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a>, our research supports the notion that the <strong>distinction between "real-time” and “ex-post” is irrelevant</strong> when it comes to the impact of these technologies on fundamental rights. Ex-post identification carries in fact a higher potential of harm, as more data can be pooled from different sources to proceed to the identification. The use of such technologies for <strong>“targeted surveillance” is thus equally harmful</strong> as the practice might be considered as expansive and intrusive to an extent that it would constitute disproportionate interference with the rights to privacy and personal data protection.</p></li>
<li><p>This concerns not only the acquisition and processing of <strong>faces, but also gait, voice and other biometric or behavioural signals.</strong></p></li>
</ul>
<p><strong>2. The EU should strengthen transparency and accountability of biometric and behavioural recognition technologies</strong></p>
<ul>
@ -696,10 +779,10 @@
<p>The intrusiveness of the system, and its impact on fundamental rights is best exemplified by its deployment in the Xinjiang province. The province capital, Urumqi, is chequered with <strong>checkpoints and identification stations</strong>. Citizens need to submit to facial recognition ID checks in supermarkets, hotels, train stations, highway stations and several other public spaces (Chin and Bürge 2017). The information collected through the cameras is centralised and matched against other <strong>biometric data</strong> such as <strong>DNA samples</strong> and <strong>voice samples</strong>. This allows the government to attribute <strong>trust-worthiness scores</strong> (trustworthy, average, untrustworthy) and thus generate a list of individuals that can become candidates for detention (Wang 2018).</p>
<p>European countries’ deployments are far from the Chinese experience. But the companies involved in China’s pervasive digital surveillance network (such as <strong>Tencent</strong>, <strong><a class="maplink" data-title="Dahua Technologies">Dahua Technology</a></strong>, <strong><a class="maplink" data-title="Hikvision">Hikvision</a></strong>, <strong>SenseTime</strong>, <strong>ByteDance</strong> and <strong><a class="maplink" data-title="Huawei">Huawei</a></strong>) are exporting their know-how to Europe, under the form of “<strong>safe city” packages</strong>. <strong><a class="maplink" data-title="Huawei">Huawei</a></strong> is one of the most active in this regard. On the European continent, the city of Belgrade has for example deployed an extensive communication network of more than 1.000 cameras which collect up to 10 body and facial attributes (Stojkovski 2019). The cameras, deployed on poles, major traffic crossings and a large number of public spaces allow the Belgrade police to monitor large parts of the city centre, collect <strong>biometric information</strong> and communicate it directly to police officers deployed in the field. Belgrade has the most advanced deployment of <a class="maplink" data-title="Huawei">Huawei</a>’s surveillance technologies on the European continent, but similar projects are being implemented by other corporations – including the <strong>European companies <a class="maplink" data-title="Thales">Thales</a>, <a class="maplink" data-title="Engie Ineo">Engie Ineo</a> or <a class="maplink" data-title="IDEMIA">Idemia</strong> – in other European cities and many “Safe City” deployments are planned soon in EU countries such as France, Italy, Spain, Malta, and Germany (Hillman and McCalpin 2019). Furthermore, contrary to the idea China would be the sole exporter of Remote Biometric Identification technologies, EU companies have substantially developed their exports in this domain over the last years (Wagner 2021)</p>
<p>The turning point of public debates on facial recognition in Europe was probably <strong>the <a class="maplink" data-title="Clearview AI">Clearview AI</a> controversy</strong> in 2019-2020. <strong><a class="maplink" data-title="Clearview AI">Clearview AI</a></strong>, a company founded by Hoan Ton-That and Richard Schwartz in the United States, maintained a relatively secret profile until a New York Times article revealed in late 2019 that it was selling <strong>facial recognition technology</strong> to law enforcement.  In February 2020, it was reported that the client list of <a class="maplink" data-title="Clearview AI">Clearview AI</a> had been stolen, and a few days later the details of the list were leaked (Mac, Haskins, and McDonald 2020). To the surprise of many in Europe, in addition to US government agencies and corporations, it appeared that the <strong>Metropolitan Police Service</strong> <strong>(London, UK)</strong>, as well as <strong>law enforcement from Belgian, Denmark, Finland, France, Ireland, <a class="maplink" data-title="Carabinieri">Italy</a>, Latvia, Lithuania, Malta, the Netherlands, Norway, Portugal, Serbia, Slovenia, Spain, Sweden, and Switzerland were on the client list.</strong> The controversy grew larger as it emerged that <a class="maplink" data-title="Clearview AI">Clearview AI</a> had (semi-illegally) harvested a large number of images from social media platforms such as <strong><a class="maplink" data-title="Facebook">Facebook</a>, YouTube</strong> and <strong>Twitter</strong> in order to constitute the datasets against which clients were invited to carry out searches (Mac, Haskins, and McDonald 2020).</p>
<p>European countries’ deployments are far from the Chinese experience. But the companies involved in China’s pervasive digital surveillance network (such as <strong>Tencent</strong>, <strong><a class="maplink" data-title="Dahua Technologies">Dahua Technology</a></strong>, <strong><a class="maplink" data-title="Hikvision">Hikvision</a></strong>, <strong>SenseTime</strong>, <strong>ByteDance</strong> and <strong><a class="maplink" data-title="Huawei">Huawei</a></strong>) are exporting their know-how to Europe, under the form of “<strong>safe city” packages</strong>. <strong><a class="maplink" data-title="Huawei">Huawei</a></strong> is one of the most active in this regard. On the European continent, the city of Belgrade has for example deployed an extensive communication network of more than 1.000 cameras which collect up to 10 body and facial attributes (Stojkovski 2019). The cameras, deployed on poles, major traffic crossings and a large number of public spaces allow the Belgrade police to monitor large parts of the city centre, collect <strong>biometric information</strong> and communicate it directly to police officers deployed in the field. Belgrade has the most advanced deployment of <a class="maplink" data-title="Huawei">Huawei</a>’s surveillance technologies on the European continent, but similar projects are being implemented by other corporations – including the <strong>European companies <a class="maplink" data-title="Thales">Thales</a>, <a class="maplink" data-title="Engie Ineo">Engie Ineo</a> or <a class="maplink" data-title="IDEMIA">Idemia</strong> – in other European cities and many “Safe City” deployments are planned soon in EU countries such as France, Italy, Spain, <a class="maplink" data-title="Safe City Malta">Malta</a>, and Germany (Hillman and McCalpin 2019). Furthermore, contrary to the idea China would be the sole exporter of Remote Biometric Identification technologies, EU companies have substantially developed their exports in this domain over the last years (Wagner 2021)</p>
<p>The turning point of public debates on facial recognition in Europe was probably <strong>the <a class="maplink" data-title="Clearview AI">Clearview AI</a> controversy</strong> in 2019-2020. <strong><a class="maplink" data-title="Clearview AI">Clearview AI</a></strong>, a company founded by Hoan Ton-That and Richard Schwartz in the United States, maintained a relatively secret profile until a New York Times article revealed in late 2019 that it was selling <strong>facial recognition technology</strong> to law enforcement.  In February 2020, it was reported that the client list of <a class="maplink" data-title="Clearview AI">Clearview AI</a> had been stolen, and a few days later the details of the list were leaked (Mac, Haskins, and McDonald 2020). To the surprise of many in Europe, in addition to US government agencies and corporations, it appeared that the Metropolitan Police Service (London, UK), as well as <strong>law enforcement from Belgian, Denmark, Finland, France, Ireland, <a class="maplink" data-title="Carabinieri">Italy</a>, Latvia, Lithuania, <a class="maplink" data-title="Maltese State">Malta</a>, the <a class="maplink" data-title="Dutch Police">Netherlands</a>, Norway, Portugal, Serbia, <a class="maplink" data-title="Slovenian Police">Slovenia</a>, Spain, <a class="maplink" data-title="Use of Clearview AI in Sweden">Sweden</a>, and Switzerland were on the client list.</strong> The controversy grew larger as it emerged that <a class="maplink" data-title="Clearview AI">Clearview AI</a> had (semi-illegally) harvested a large number of images from social media platforms such as <strong><a class="maplink" data-title="Facebook">Facebook</a>, YouTube</strong> and <strong>Twitter</strong> in order to constitute the datasets against which clients were invited to carry out searches (Mac, Haskins, and McDonald 2020).</p>
<p>The news of the hacking strengthened a strong push-back movement against the development of facial recognition technology by companies such as <a class="maplink" data-title="Clearview AI">Clearview AI</a>, as well as their use by government agencies. In 2018, <strong>Massachusetts Institute of Technology</strong> (MIT) scholar and <strong><a class="maplink" data-title="Algorithmic Justice League">Algorithmic Justice League</a></strong> founder <strong>Joy Buolamwini</strong> together with <strong>Temnit Gebru</strong> had published the report <em>Gender Shades</em> (Buolamwini and Gebru 2018), in which they assessed the racial bias in the face recognition datasets and algorithms used by companies such as <a class="maplink" data-title="IBM">IBM</a> and Microsoft. Buolamwini and Gebru found that <strong>algorithms performed generally worse on darker-skinned faces, and in particular darker-skinned females, with error rates up to 34% higher than lighter-skinned males</strong> (Najibi 2020). <a class="maplink" data-title="IBM">IBM</a> and Microsoft responded by amending their systems, and a re-audit showed less bias. Not all companies responded equally. <strong>Amazon’s Rekognition</strong> system, which was included in the second study continued to show a 31% lower rate for darker-skinned females. The same year <strong>ACLU</strong> conducted another key study on Amazon’s Rekognition, using the pictures of <strong>members of congress against a dataset of mugshots from law enforcemen</strong>t. 28 members of Congress, <strong>largely people of colour were incorrectly matched</strong> (Snow 2018). Activists engaged lawmakers. In 2019, the Algorithmic Accountability Act allowed the Federal Trade Commission to regulate private companies’ uses of facial recognition. In 2020, several companies, including <a class="maplink" data-title="IBM">IBM</a>, Microsoft, and Amazon, announced a moratorium on the development of their facial recognition technologies. Several US cities, including <strong>Boston</strong>, <strong>Cambridge</strong> (Massachusetts) <strong>San Francisco</strong>, <strong>Berkeley</strong>, <strong>Portland</strong> (Oregon), have also banned their police forces from using the technology.</p>
<p>The news of the hacking strengthened a strong push-back movement against the development of facial recognition technology by companies such as <a class="maplink" data-title="Clearview AI">Clearview AI</a>, as well as their use by government agencies. In 2018, <strong>Massachusetts Institute of Technology</strong> (MIT) scholar and <strong><a class="maplink" data-title="Algorithmic Justice League">Algorithmic Justice League</a></strong> founder <strong>Joy Buolamwini</strong> together with <strong>Temnit Gebru</strong> had published the report <em>Gender Shades</em> (Buolamwini and Gebru 2018), in which they assessed the racial bias in the face recognition datasets and algorithms used by companies such as <a class="maplink" data-title="IBM">IBM</a> and Microsoft. Buolamwini and Gebru found that <strong>algorithms performed generally worse on darker-skinned faces, and in particular darker-skinned females, with error rates up to 34% higher than lighter-skinned males</strong> (Najibi 2020). <a class="maplink" data-title="IBM">IBM</a> and Microsoft responded by amending their systems, and a re-audit showed less bias. Not all companies responded equally. <strong>Amazon’s Rekognition</strong> system, which was included in the second study continued to show a 31% lower rate for darker-skinned females. The same year <strong>ACLU</strong> conducted another key study on Amazon’s Rekognition, using the pictures of <strong>members of congress against a dataset of mugshots from law enforcement</strong>. 28 members of Congress, <strong>largely people of colour were incorrectly matched</strong> (Snow 2018). A number of organizations seized the problem as a policy issue (<strong><a class="maplink" data-title="Black in AI">Black in AI</a></strong>, <strong><a class="maplink" data-title="Algorithmic Justice League">Algorithmic Justice League</a>, <a class="maplink" data-title="Data for Black Lives">Data for Black Lives</a></strong>) and some engaged lawmakers. In 2019, the Algorithmic Accountability Act allowed the Federal Trade Commission to regulate private companies’ uses of facial recognition. In 2020, several companies, including <a class="maplink" data-title="IBM">IBM</a>, Microsoft, and Amazon, announced a moratorium on the development of their facial recognition technologies. Several US cities, including <strong>Boston</strong>, <strong>Cambridge</strong> (Massachusetts) <strong>San Francisco</strong>, <strong>Berkeley</strong>, <strong>Portland</strong> (Oregon), have also banned their police forces from using the technology.</p>
</section>
<section id="the-european-context" class="level2">
@ -708,7 +791,7 @@
<p>Legislative activity accelerated in 2018. The <strong>European Commission</strong> (2018a) published a communication <em>Artificial Intelligence for Europe</em>, in which it called for a joint legal framework for the regulation of AI-related services. Later in the year, the Commission (2018b) adopted a <em>Coordinated Plan on Artificial Intelligence</em> with similar objectives. It compelled EU member states to adopt a national strategy on artificial intelligence which should meet the EU requirements. It also allocated 20 billion euros each year for investment in AI development. (Andraško et al. 2021, 4).</p>
<p>In 2019, the <strong>Council of Europe Commissioner for Human Rights</strong> published a Recommendation entitled <em>Unboxing Artificial Intelligence: 10 steps to Protect Human Rights</em> which describes several steps for national authorities to maximise the potential of AI while preventing or mitigating the risk of its misuse. (Gonzalez Fuster 2020, 46). The same year the <strong><a class="maplink" data-title="European Union">European Union</a>’s High Level Expert Group on Artificial Intelligence (AI HLEG)</strong> adopted the <em>Ethics Guidelines for Trustworthy Artificial Intelligence</em>, a key document for the EU strategy in bringing AI within ethical standards (Nesterova 2020, 3).</p>
<p>In 2019, the <strong>Council of Europe Commissioner for Human Rights</strong> published a Recommendation entitled <em>Unboxing Artificial Intelligence: 10 steps to Protect Human Rights</em> which describes several steps for national authorities to maximise the potential of AI while preventing or mitigating the risk of its misuse. (Gonzalez Fuster 2020, 46). The same year the <strong><a class="maplink" data-title="European Union">European Union</a>’s <a class="maplink" data-title="European Union High-Level Expert Group on Artificial Intelligence (AI HLEG)">High Level Expert Group on Artificial Intelligence</a> (AI HLEG)</strong> adopted the <em>Ethics Guidelines for Trustworthy Artificial Intelligence</em>, a key document for the EU strategy in bringing AI within ethical standards (Nesterova 2020, 3).</p>
<p>In February 2020, the new <strong>European Commission</strong> went one step further in regulating matters related to AI, adopting the digital agenda package – a set of documents outlining the strategy of the EU in the digital age. Among the documents the <em>White Paper on Artificial Intelligence: a European approach to excellence and trust</em> captured most of the commission’s intentions and plans.  </p>
</section>
@ -717,7 +800,7 @@
<p>Over the past 3-4 years, positions around the use of facial recognition and more specifically the use of remote biometric identification in public space have progressively crystalised into four camps (for a more detailed analysis of the positions, see Chapter 5).</p>
<section id="active-promotion" class="level3">
<h3>Active promotion</h3>
<p>A certain number of actors, both at the national and at the local level are pushing for the development and the extension of biometric remote identification. At the local level, figures such as Nice’s (France) mayor Christian Estrosi have repeatedly challenged Data Protection Authorities, arguing for the usefulness of such technologies in the face of insecurity (for a detailed analysis, see chapter 8 in this report, see also Barelli 2018). <strong>At the national level, Biometric systems for the purposes of authentication are increasingly deployed for forensic applications</strong> among law-enforcement agencies in the <a class="maplink" data-title="European Union">European Union</a>. As we elaborate in Chapter 3, 11 out of 27 member states of the <a class="maplink" data-title="European Union">European Union</a> are already using facial recognition against biometric databases for forensic purposes and 7 additional countries are expected to acquire such capabilities in the near future. Several states that have not yet adopted such technologies seem inclined to follow the trend, and push further. Belgian Minister of Interior Pieter De Crem for example, recently declared he was in favour of the use of facial recognition both for judicial inquiries but also for live facial recognition, a much rarer instance. Such outspoken advocates of the use of RBI constitute an important voice, but do not find an echo in the EU mainstream discussions.</p>
<p>A certain number of actors, both at the national and at the local level are pushing for the development and the extension of biometric remote identification. At the local level, figures such as Nice’s (France) mayor Christian Estrosi have repeatedly challenged Data Protection Authorities, arguing for the usefulness of such technologies in the face of insecurity (for a detailed analysis, see chapter 8 in this report, see also Barelli 2018). <strong>At the national level, Biometric systems for the purposes of authentication are increasingly deployed for forensic applications</strong> among law-enforcement agencies in the <a class="maplink" data-title="European Union">European Union</a>. As we elaborate in Chapter 3, 11 out of 27 member states of the <a class="maplink" data-title="European Union">European Union</a> are already using facial recognition against biometric databases for forensic purposes and 7 additional countries are expected to acquire such capabilities in the near future. Several states that have not yet adopted such technologies seem inclined to follow the trend, and push further. Former Belgian Minister of Interior Pieter De Crem for example, recently declared he was in favour of the use of facial recognition both for judicial inquiries but also for live facial recognition, a much rarer instance. Such outspoken advocates of the use of RBI constitute an important voice, but do not find an echo in the EU mainstream discussions.</p>
</section>
<section id="support-with-safeguards" class="level3">
<h3>Support with safeguards </h3>
@ -729,7 +812,7 @@
</section>
<section id="ban" class="level3">
<h3>Ban</h3>
<p>Finally, a growing number of actors considers that there is enough information about remote biometric identification in public space to determine that they will never be able to comply to the strict requirement of the <a class="maplink" data-title="European Union">European Union</a> in terms of respect of Fundamental Rights, and as such should be banned entirely. It is the current position of the <strong>European Data Protection Supervisor (EDPS, 2021)</strong> the <strong>Council of Europe</strong> and a large coalition of NGOs, gathered under the umbrella of the <strong>European Digital Rights organisation</strong> (EDRi 2020). In the <strong>European Parliament</strong>, the position has most vocally been defended by the European Greens, but has been shared by several other voices, such as members of the Party of the European Left, the Party of European Socialists or Renew Europe (Breyer et al 2021).</p>
<p>Finally, a growing number of actors considers that there is enough information about remote biometric identification in public space to determine that they will never be able to comply to the strict requirement of the <a class="maplink" data-title="European Union">European Union</a> in terms of respect of Fundamental Rights, and as such should be banned entirely. It is the current position of the <strong>European Data Protection Supervisor (EDPS, 2021)</strong> the <strong>Council of Europe</strong> and a large coalition of NGOs (among which <strong><a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a></strong> and the collaborative project <strong>Technopolice</strong>,) gathered under the umbrella of the <strong><a class="maplink" data-title="European Digital Rights (EDRi)">European Digital Rights organisation</a></strong> (EDRi 2020). In the <strong>European Parliament</strong>, the position has most vocally been defended by the European Greens, but has been shared by several other voices, such as members of the Party of the European Left, the Party of European Socialists or Renew Europe (Breyer et al 2021).</p>
</section>
</section>
<section id="lack-of-transparency-and-the-stifling-of-public-debate" class="level2">
@ -795,7 +878,7 @@
</section>
<section id="people-tracking-and-counting" class="level3">
<h3>People tracking and counting </h3>
<p>This is perhaps the form of person tracking with which the least information about an individual is stored. An <strong>object detection algorithm</strong> estimates the presence and position of individuals on a camera image. These positions are stored or counted and used for further metrics. It is used to count <strong>passers-by in city centres</strong>, and for a <strong>one-and-a-half-meter social distancing monitor in Amsterdam</strong><a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a>. See also the case study in this document on the <a class="maplink" data-title="Data-lab Burglary-free Neighbourhood">Burglary-Free Neighbourhood</a> in Rotterdam (CHAPTER 7), which goes into more detail about the use of the recorded trajectories of individuals to label anomalous behaviour.</p>
<p>This is perhaps the form of person tracking with which the least information about an individual is stored. An <strong>object detection algorithm</strong> estimates the presence and position of individuals on a camera image. These positions are stored or counted and used for further metrics. It is used to count <strong>passers-by in city centres</strong>, and for a <strong>one-and-a-half-meter social distancing monitor in Amsterdam</strong><a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a>. See also the case study in this document on the <a class="maplink" data-title="Data-lab Burglary-free Neighbourhood">Burglary-Free Neighbourhood</a> in Rotterdam (CHAPTER 7), which goes into more detail about the use of the recorded trajectories of individuals to label anomalous behaviour.</p>
</section>
<section id="emotion-recognition." class="level3">
<h3>Emotion recognition. </h3>
@ -807,7 +890,7 @@
</section>
<section id="audio-recognition" class="level3">
<h3>Audio recognition </h3>
<p>From a technological perspective, neural networks process audio relatively similarly to how video is processed: rather than feeding an image, a spectrogram is used as input for the network. However, under the GDPR, recording conversations, is illegal in the <a class="maplink" data-title="European Union">European Union</a> without informed consent of the participants. In order to adhere to these regulations, on some occasions, only particular frequencies are recorded and processed. For example, in the <a class="maplink" data-title="Data-lab Burglary-free Neighbourhood">Burglary-Free Neighbourhood</a> in Rotterdam (CHAPTER 7), only two frequencies are used to classify audio; making conversations indiscernible while being able to discern shouting or the breaking of glass<a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a>.</p>
<p>From a technological perspective, neural networks process audio relatively similarly to how video is processed: rather than feeding an image, a spectrogram is used as input for the network. However, under the GDPR, recording conversations, is illegal in the <a class="maplink" data-title="European Union">European Union</a> without informed consent of the participants. In order to adhere to these regulations, on some occasions, only particular frequencies are recorded and processed. For example, in the <strong><a class="maplink" data-title="Data-lab Burglary-free Neighbourhood">Burglary-Free Neighbourhood</a> in Rotterdam (Netherlands)</strong> (CHAPTER 7), only two frequencies are used to classify audio; making conversations indiscernible while being able to discern shouting or the breaking of glass<a href="#fn4" class="footnote-ref" id="fnref4" role="doc-noteref"><sup>4</sup></a>. Another initiative using audio in to enhance the surveillance camera is the <strong>Living Lab International Zone</strong> project in the Hague (Netherlands), a collaboration between a broad range of partners<a href="#fn5" class="footnote-ref" id="fnref5" role="doc-noteref"><sup>5</sup></a>.</p>
</section>
</section>
<section id="how-does-image-based-remote-biometric-identification-work" class="level2">
@ -816,26 +899,26 @@
<section id="image-acquisition-controlled-and-uncontrolled-images" class="level3">
<h3>Image acquisition: Controlled and uncontrolled images</h3>
<p>Facial recognition begins with an image. An image which will be subject to the algorithm’s scrutiny. <strong>Controlled images</strong> are images that are captured for the purpose of processing, aimed at optimal positions and lighting conditions. They are for example taken at a police station, or at a photographer’s studio with strict requirements, and are either contained in databases that precede the introduction of a facial recognition system (e.g., driver’s license databases) or are specifically designed to match high criteria of biometric systems (i.e., photographs for biometric passports). <strong>Uncontrolled images</strong> are images that are captured outside of specific requirement, collected for example through social media scraping or video surveillance.</p>
<p>When it comes to the acquisition technologies (cameras) for uncontrolled images, over the past decades, the main evolution in terms of video has been the passage from analogue video to digital video, the latter allowing images to be processed through computers. As in the realm of consumer cameras, the initial race was for better definition (calculated in terms of megapixels). “Smart” camera systems require a slightly higher resolution than standard video surveillance systems in order to guarantee a minimum of 300 PPM to adequately feed the software (IPVM Team 2020, 5). But overall, the average camera does not exceed a definition of 4 megapixels and are more often in the area of 2 megapixels (which yields a 1080p or HD resolution)<a href="#fn4" class="footnote-ref" id="fnref4" role="doc-noteref"><sup>4</sup></a>. <strong>The quality of capture</strong>, especially in non-cooperative scenarios, is determined by two main external variables: the angle of the face relative to the camera (front, side, back, top) and the lighting conditions (bright daylight, dark night). In recent years, manufacturers have added an additional infra-red channel to the red-green-blue (RGB) video channels in order to increase detail accuracy in low-light conditions.</p>
<p>When it comes to the acquisition technologies (cameras) for uncontrolled images, over the past decades, the main evolution in terms of video has been the passage from analogue video to digital video, the latter allowing images to be processed through computers. As in the realm of consumer cameras, the initial race was for better definition (calculated in terms of megapixels). “Smart” camera systems require a slightly higher resolution than standard video surveillance systems in order to guarantee a minimum of 300 PPM to adequately feed the software (IPVM Team 2020, 5). But overall, the average camera does not exceed a definition of 4 megapixels and are more often in the area of 2 megapixels (which yields a 1080p or HD resolution)<a href="#fn6" class="footnote-ref" id="fnref6" role="doc-noteref"><sup>6</sup></a>. <strong>The quality of capture</strong>, especially in non-cooperative scenarios, is determined by two main external variables: the angle of the face relative to the camera (front, side, back, top) and the lighting conditions (bright daylight, dark night). In recent years, manufacturers have added an additional infra-red channel to the red-green-blue (RGB) video channels in order to increase detail accuracy in low-light conditions.</p>
</section>
<section id="what-makes-systems-smart-image-processing-algorithms" class="level3">
<h3>What makes systems “smart”: image processing algorithms</h3>
<p>The processing of the photographic or video image by a specific software application is where the “smart” processing happens. Broadly speaking video surveillance technology can be split in two key historical moments: before machine learning, and after machine learning.</p>
<p><strong>Video motion detection (VMD) and heuristic filters.</strong> The early smart technologies relied on simple motion detection algorithms which compared pixel changes from one image to the next (Quevillon 2012). The problem is that any movement (the leaves of a tree) or change of light (a car passing in the night) can trigger the systems. <strong>Heuristic filters</strong> were thus added to VMD systems in order to give additional parameters to the system (amount and size of pixel changing etc.). Both systems were highly inefficient and prone to trigger false alarms, making such technologies unattractive. The main problem was that only pre-established changes hard coded by humans would be detected by the systems.</p>
<p><strong>Machine learning.</strong> Machine learning revolutionised image-based biometric identification. Machine learning is an automated process through which the software application will be programmed to recognise particular patterns, based on a dataset it is “trained” on. There are three ways in which this configuration of the machine learning model can be controlled: supervised, semi-supervised or unsupervised. <strong>Supervised machine learning</strong> consists of teaching the system to recognise people, cars, guns, or any other object by feeding it an annotated dataset of such objects. It is supervised because humans “supervise” how the computer learns, by annotating the dataset (“this is a car”, “this is a gun” etc.). The categories of the annotations (cars, guns, etc.) will thus be the only ones that the system will be able to recognise (if only cars and guns are annotated, the system won’t in such a case recognise cats). Most video surveillance systems use supervised machine learning (IPVM Team 2021a, 11)<strong>. Unsupervised machine learning</strong> lets the system cluster objects by itself. The advantage is the open-endedness of the systems (meaning they can generate categories of objects not anticipated in the training dataset), but the disadvantage is that algorithms can potentially cluster objects along irrelevant criteria for the task (for example clustering red motorcycles, cars, and trucks in one group and green ones in another, as opposed to creating one cluster for all motorcycles, one for cars and one for trucks). For this reason, <strong>semi-supervised machine learning</strong>, where only a small part of the data is labelled, can be used. Currently not widely in use, unsupervised machine learning is a growing trend in the video surveillance sector (IPVM Team 2021a, 12–13).</p>
<p>Both supervised and unsupervised learning exist in many shapes and sizes. For example, the Viola-Jones object detection algorithm<a href="#fn5" class="footnote-ref" id="fnref5" role="doc-noteref"><sup>5</sup></a> from 2001, which made real-time face detection viable, is a supervised algorithm. Contemporary developments in video processing focus on using various kinds of artificial <strong>neural networks</strong> (i.e., <strong>convolutional neural networks</strong>, <strong>recurrent neural networks</strong>) to classify images and videos. These networks can be trained either <strong>supervised</strong>, <strong>semi-supervised</strong> or <strong>unsupervised</strong> depending on their configuration.</p>
<p>Both supervised and unsupervised learning exist in many shapes and sizes. For example, the Viola-Jones object detection algorithm<a href="#fn7" class="footnote-ref" id="fnref7" role="doc-noteref"><sup>7</sup></a> from 2001, which made real-time face detection viable, is a supervised algorithm. Contemporary developments in video processing focus on using various kinds of artificial <strong>neural networks</strong> (i.e., <strong>convolutional neural networks</strong>, <strong>recurrent neural networks</strong>) to classify images and videos. These networks can be trained either <strong>supervised</strong>, <strong>semi-supervised</strong> or <strong>unsupervised</strong> depending on their configuration.</p>
</section>
<section id="machine-learning-and-operational-datasets" class="level3">
<h3><strong>Machine learning</strong> and operational datasets</h3>
<p>Remote biometric identification and classification relies in large part on datasets, for two key but distinct moments of their operation.</p>
<p><strong>Machine learning datasets.</strong> These are the datasets used to train models through <strong>machine learning.</strong> We find three categories of such datasets. <strong>Publicly available datasets</strong> for object detection such as COCO, ImageNet, Pascal VOC include a varying number of images labelled in a range of categories, these can be used to train algorithms to detect for example people on an image (IPVM Team 2021a, 27). The most used open-source datasets for surveillance technologies are Celeb 500k, MS-Celeb-1Million-Cleaned, Labeled Faces in the Wild, VGG Face 2, DeepGlint Asian Celeb, IMDB-Face, IMDB-Wiki, CelebA, Diveface, Flickr faces and the IARPA Janus Benchmark (IPVM Team 2021b, 7). Many of these datasets also function as a public benchmark, against which the performance and accuracy of various algorithms is measured. For example, Labeled Faces in the Wild, the COCO dataset and <a class="maplink" data-title="NIST">NIST</a> present such leaderboards on their website<a href="#fn6" class="footnote-ref" id="fnref6" role="doc-noteref"><sup>6</sup></a>. <strong>Government datasets</strong> are generally collections of images available to a government for other purposes (driver’s license, passport, or criminal record photo datasets). While in Europe most of these datasets are not accessible to the public, in China and in the US, they are made available for testing and training purposes to private companies, such as the Multiple Encounter Dataset (NIST, 2010). Finally <strong>proprietary datasets</strong> may be developed by providers for their specific applications.</p>
<p><strong>Machine learning datasets.</strong> These are the datasets used to train models through <strong>machine learning.</strong> We find three categories of such datasets. <strong>Publicly available datasets</strong> for object detection such as COCO, ImageNet, Pascal VOC include a varying number of images labelled in a range of categories, these can be used to train algorithms to detect for example people on an image (IPVM Team 2021a, 27). The most used open-source datasets for surveillance technologies are Celeb 500k, MS-Celeb-1Million-Cleaned, Labeled Faces in the Wild, VGG Face 2, DeepGlint Asian Celeb, IMDB-Face, IMDB-Wiki, CelebA, Diveface, Flickr faces and the IARPA Janus Benchmark (IPVM Team 2021b, 7). Many of these datasets also function as a public benchmark, against which the performance and accuracy of various algorithms is measured. For example, Labeled Faces in the Wild, the COCO dataset and <a class="maplink" data-title="NIST">NIST</a> present such leaderboards on their website<a href="#fn8" class="footnote-ref" id="fnref8" role="doc-noteref"><sup>8</sup></a>. <strong>Government datasets</strong> are generally collections of images available to a government for other purposes (driver’s license, passport, or criminal record photo datasets). While in Europe most of these datasets are not accessible to the public, in China and in the US, they are made available for testing and training purposes to private companies, such as the Multiple Encounter Dataset (NIST, 2010). Finally <strong>proprietary datasets</strong> may be developed by providers for their specific applications.</p>
<p><strong>Machine learning models.</strong> In the machine learning process, an algorithm gets iteratively configured for the optimal output, based on the particular dataset that it is fed with. This can be a neural network, but also e.g., the aforementioned Viola-Jones’ object detector algorithm. The <strong>model</strong> is the final configuration of this learning process. As such, it does not contain the images of the dataset in and of themselves. Rather, it represents the abstractions the algorithm “learned” over time. In other words, the model operationalises the machine learning dataset. For example, the YOLO object detection algorithm yields different results when it is trained on either the COCO or the model (in conjunction with the algorithm) which determines the translation of an image into a category, or of the image of a face into its embedding.</p>
<p><strong>Operational datasets, or image databases.</strong> Datasets used in training machine learning models should be distinguished from matching or operational datasets which are the “watchlists” of for example criminals, persons of interest or other lists of individuals against which facial recognition searches will be performed – whether these are in real time or post hoc. These datasets contain pre-processed images of individuals on the watchlist, and store the numerical representations of these faces, their feature vectors or <em>embedding</em>, in an index for fast retrieval and comparison with the queried features (using for example k-Nearest Neighbour or Support Vector Machines). Face or object detection models do not use such a dataset.</p>
</section>
<section id="availability" class="level3">
<h3>Availability</h3>
<p>Facial recognition algorithms can be developed in-house, taken from an open-source repository, or purchased (IPVM Team 2021b, 14). Popular <strong>open-source facial recognition</strong> implementations include OpenCV, Face_pytorch, OpenFace and Insightface. Many of these software libraries are developed at universities or implement algorithms and neural network architectures presented in academic papers. They are free, and allow for a great detail of customisation, but require substantial programming skills to be implemented in a surveillance system. Moreover, when using such software, the algorithms run on one’s own hardware which provides the developer with more control, but also requires more maintenance.</p>
<p><strong>Proprietary facial recognition.</strong> There are three possible routes for the use of proprietary systems: There are <strong>“turnkey”</strong> systems sold by manufacturers such as <strong><a class="maplink" data-title="Hikvision">Hikvision</a></strong>, <strong><a class="maplink" data-title="Dahua Technologies">Dahua</a></strong>, <strong><a class="maplink" data-title="AnyVision">AnyVision</a></strong> or <strong><a class="maplink" data-title="Briefcam Ltd">Briefcam</a></strong>. Those integrate the software and hardware, and as such can be directly deployed by the client. <strong>Algorithm developers</strong> such as <strong>Amazon AWS Rekognition</strong> (USA), <strong><a class="maplink" data-title="NEC">NEC</a></strong> (Japan), <strong>NTechlab</strong> (Russia), <strong><a class="maplink" data-title="Paravision">Paravision</a></strong> (USA) allow to implement their algorithms and customise them to one’s needs, and finally there are <strong>“cloud” API systems</strong>, a sub-set of the former category, where the algorithm is hosted in a datacentre and is accessed remotely (IPVM Team 2021b, 16). The latter type of technology bears important legal ramifications, as the data may travel outside of national or European jurisdictions. It should be noted that many of the proprietary products are based on similar algorithms and network architectures as their open-source counterparts (OpenCV, 2021). Contrary to the open-source software, it is generally unclear which datasets of images have been used to train the proprietary algorithms.</p>
<p><strong>Proprietary facial recognition.</strong> There are three possible routes for the use of proprietary systems: There are <strong>“turnkey”</strong> systems sold by manufacturers such as <strong><a class="maplink" data-title="Hikvision">Hikvision</a></strong>, <strong><a class="maplink" data-title="Dahua Technologies">Dahua</a></strong>, <strong><a class="maplink" data-title="AnyVision">AnyVision</a></strong> or <strong><a class="maplink" data-title="Briefcam Ltd">Briefcam</a></strong>. Those integrate the software and hardware, and as such can be directly deployed by the client. <strong>Algorithm developers</strong> such as <strong>Amazon AWS Rekognition</strong> (USA), <strong><a class="maplink" data-title="NEC">NEC</a></strong> (Japan), <strong><a class="maplink" data-title="Ntech Lab">NTechlab</a></strong> (Russia), <strong><a class="maplink" data-title="Paravision">Paravision</a></strong> (USA) allow to implement their algorithms and customise them to one’s needs, and finally there are <strong>“cloud” API systems</strong>, a sub-set of the former category, where the algorithm is hosted in a datacentre and is accessed remotely (IPVM Team 2021b, 16). The latter type of technology bears important legal ramifications, as the data may travel outside of national or European jurisdictions. It should be noted that many of the proprietary products are based on similar algorithms and network architectures as their open-source counterparts (OpenCV, 2021). Contrary to the open-source software, it is generally unclear which datasets of images have been used to train the proprietary algorithms.</p>
</section>
</section>
<section id="technical-limits-problems-and-challenges-of-facial-recognition" class="level2">
@ -879,19 +962,19 @@
<p>A broad range of deployments, which we consider in this first section, is not aimed at surveillance, but at authentication (see section 2.3 in this report), namely making sure that the person in front of the security camera is who they say they are.</p>
<section id="live-authentication" class="level3">
<h3>Live authentication</h3>
<p>As in the cases of the use of <a class="maplink" data-title="Cisco Systems">Cisco systems</a> powered FRT in two pilot projects in <strong><a class="maplink" data-title="Facial Recognition Pilot in High School (Nice)">high schools of Nice</a></strong> (see section 8.1) <strong>and <a class="maplink" data-title="Facial Recognition Pilot in High School (Marseille)">Marseille</a> (France)</strong><a href="#fn7" class="footnote-ref" id="fnref7" role="doc-noteref"><sup>7</sup></a>, or as in the case of the <strong><a class="maplink" data-title="Facial Recognition Pilot in High School (Marseille)">Anderstorp Upper Secondary School</a> in Skelleftea (Sweden)</strong><a href="#fn8" class="footnote-ref" id="fnref8" role="doc-noteref"><sup>8</sup></a>, the aim of these projects was to identify students who could have access to the premises. School-wide biometric databases were generated and populated with students’ portraits. Gates were fitted with cameras connected to facial recognition technology and allowed access only to recognised students. Another documented use has been for the <strong><a class="maplink" data-title="Home Quarantine App Hungary">Home Quarantine App (Hungary)</a></strong>, in which telephone cameras are used by authorities to verify the identity of the persons logged into the app (see also section 10.1).</p>
<p>As in the cases of the use of <a class="maplink" data-title="Cisco Systems">Cisco systems</a> powered FRT in two pilot projects in <strong><a class="maplink" data-title="Facial Recognition Pilot in High School (Nice)">high schools of Nice</a></strong> (see section 8.1) <strong>and <a class="maplink" data-title="Facial Recognition Pilot in High School (Marseille)">Marseille</a> (France)</strong><a href="#fn9" class="footnote-ref" id="fnref9" role="doc-noteref"><sup>9</sup></a>, or as in the case of the <strong><a class="maplink" data-title="Facial Recognition in Anderstorp Upper Secondary School (Skelleftea, Sweden)">Anderstorp Upper Secondary School</a> in Skelleftea (Sweden)</strong><a href="#fn10" class="footnote-ref" id="fnref10" role="doc-noteref"><sup>10</sup></a>, the aim of these projects was to identify students who could have access to the premises. School-wide biometric databases were generated and populated with students’ portraits. Gates were fitted with cameras connected to facial recognition technology and allowed access only to recognised students. Another documented use has been for the <strong><a class="maplink" data-title="Home Quarantine App Hungary">Home Quarantine App (Hungary)</a></strong>, in which telephone cameras are used by authorities to verify the identity of the persons logged into the app (see also section 10.1).</p>
<p>In these deployments, people must submit themselves to the camera in order to be identified and gain access. While these techniques of identification pose <strong>important threats to the privacy of the concerned small groups of users</strong> (in both high school cases, DPAs banned the use of FRTs), and run the risk of false positives (unauthorised people recognised as authorised) or false negatives (authorised people not recognised as such) <strong>the risk of biometric mass surveillance strictly speaking is low to non-existent because of the nature of the acquisition of images and other sensor-based data.</strong></p>
<p>However, other forms of live authentication tie in with surveillance practices, in particular various forms of <strong>blacklisting</strong>. With blacklisting the face of every passer-by is compared to a list of faces of individuals who have been rejected access to the premises. In such an instance, people do not have to be identified, as long as an image of their face is provided. This has been used in public places, for example in the case of the <a class="maplink" data-title="Korte Putstraat (Stopped)">Korte Putstraat</a> in the Dutch city of 's-Hertogenbosch: during the carnival festivities of 2019 two people were rejected access to the street after they were singled out by the system (Gotink, 2019). It is unclear how many false positives were generated during this period. Other cases of blacklisting can be found at, for example, access control at various football stadiums in Europe, see also section 3.3. In many cases of blacklisting, individuals do not enrol voluntarily.</p>
</section>
<section id="forensic-authentication" class="level3">
<h3>Forensic authentication</h3>
<p>Biometric systems for the purposes of authentication are also increasingly deployed for <strong>forensic applications</strong> among law-enforcement agencies in the European Union. The typical scenario for the use of such technologies is to match the photograph of a suspect (extracted, for example, from previous records or from CCTV footage) against an existing dataset of known individuals (e.g., a national biometric database, a driver’s license database, etc.). (TELEFI, 2021). The development of these forensic authentication capabilities is particularly relevant to this study, because it entails making large databases ready for searches on the basis of biometric information.</p>
<p>To date, <strong>11 out of 27 member states of the <a class="maplink" data-title="European Union">European Union</a></strong> are using facial recognition against biometric databases for forensic purposes: <strong>Austria</strong> (<a class="maplink" data-title="EDE (AFR used by Austrian Criminal Intelligence Service)">EDE</a>)<a href="#fn9" class="footnote-ref" id="fnref9" role="doc-noteref"><sup>9</sup></a>, <strong>Finland</strong> (<a class="maplink" data-title="KASTU (Finland)">KASTU</a>)<a href="#fn10" class="footnote-ref" id="fnref10" role="doc-noteref"><sup>10</sup></a>, <strong>France</strong> (<a class="maplink" data-title="Deployment of TAJ">TAJ</a>)<a href="#fn11" class="footnote-ref" id="fnref11" role="doc-noteref"><sup>11</sup></a>, <strong>Germany</strong> (<a class="maplink" data-title="German central criminal information system INPOL">INPOL</a>)<a href="#fn12" class="footnote-ref" id="fnref12" role="doc-noteref"><sup>12</sup></a>, <strong>Greece</strong> (<a class="maplink" data-title="Facial Recognition in Greece (Law Enforcement)">Mugshot Database</a>)<a href="#fn13" class="footnote-ref" id="fnref13" role="doc-noteref"><sup>13</sup></a>, <strong>Hungary</strong> (<a class="maplink" data-title="NEC Face Recognition Search Engine in Hungary">Facial Image Registry</a>)<a href="#fn14" class="footnote-ref" id="fnref14" role="doc-noteref"><sup>14</sup></a>, <strong>Italy</strong> (<a class="maplink" data-title="AFIS (Deployment, Italy)">AFIS</a>)<a href="#fn15" class="footnote-ref" id="fnref15" role="doc-noteref"><sup>15</sup></a>, <strong>Latvia</strong> (<a class="maplink" data-title="BDAS Deployment (Latvia)">BDAS</a>)<a href="#fn16" class="footnote-ref" id="fnref16" role="doc-noteref"><sup>16</sup></a>, <strong>Lithuania</strong> (<a class="maplink" data-title="HDR (Deployment, Lithuania)">HDR</a>)<a href="#fn17" class="footnote-ref" id="fnref17" role="doc-noteref"><sup>17</sup></a>, <strong>Netherlands</strong> (<a class="maplink" data-title="CATCH">CATCH</a>)<a href="#fn18" class="footnote-ref" id="fnref18" role="doc-noteref"><sup>18</sup></a> and <strong>Slovenia</strong> (<a class="maplink" data-title="VeriLook (and Face Trace) in Slovenia">Record of Photographed Persons</a>)<a href="#fn19" class="footnote-ref" id="fnref19" role="doc-noteref"><sup>19</sup></a> (TELEFI 2021).</p>
<p><strong>Seven additional countries</strong> are expected to acquire such capabilities in the near future: <strong>Croatia</strong> (<a class="maplink" data-title="ABIS (Deployment, Croatia)">ABIS</a>)<a href="#fn20" class="footnote-ref" id="fnref20" role="doc-noteref"><sup>20</sup></a>, <strong>Czech Republic</strong> (<a class="maplink" data-title="CBIS (deployment, Czech Republic)">CBIS</a>)<a href="#fn21" class="footnote-ref" id="fnref21" role="doc-noteref"><sup>21</sup></a>, <strong>Portugal</strong> (<a class="maplink" data-title="AFIS (Deployment, Portugal)">AFIS</a>) <strong>Romania</strong> (<a class="maplink" data-title="Romanian Police use of Facial Recognition (NBIS)">NBIS</a>)<a href="#fn22" class="footnote-ref" id="fnref22" role="doc-noteref"><sup>22</sup></a>, <strong>Spain</strong> (<a class="maplink" data-title="ABIS (Deployment, Spain)">ABIS</a>), <strong>Sweden</strong> (<a class="maplink" data-title="Facial Recognition National Mugshot Database (Sweden)">National Mugshot Database</a>), <strong>Cyprus</strong> (<a class="maplink" data-title="Facial Recognition in Cyprus (Law Enforcement)">ISIS Faces</a>)<a href="#fn23" class="footnote-ref" id="fnref23" role="doc-noteref"><sup>23</sup></a>, <strong>Estonia</strong> (<a class="maplink" data-title="ABIS (Deployment, Estonia)">ABIS</a>) (TELEFI 2021).</p>
<p>When it comes to international institutions, <strong><a class="maplink" data-title="Interpol">Interpol</a></strong> (2020) has a facial recognition system (<a class="maplink" data-title="IFRS (Interpol)">IFRS</a>)<a href="#fn24" class="footnote-ref" id="fnref24" role="doc-noteref"><sup>24</sup></a>, based on facial images received from more than 160 countries. <strong><a class="maplink" data-title="Europol">Europol</a></strong> has two sub-units which use the facial recognition search tool and database known as <a class="maplink" data-title="FACE Deployment by EUROPOL">FACE</a>: the European Counter Terrorism Center (ECTC) and the European Cybercrime Center (ECC). (TELEFI, 2021 149-153) (Europol 2020)</p>
<p>To date, <strong>11 out of 27 member states of the <a class="maplink" data-title="European Union">European Union</a></strong> are using facial recognition against biometric databases for forensic purposes: <strong>Austria</strong> (<a class="maplink" data-title="EDE (AFR used by Austrian Criminal Intelligence Service)">EDE</a>)<a href="#fn11" class="footnote-ref" id="fnref11" role="doc-noteref"><sup>11</sup></a>, <strong>Finland</strong> (<a class="maplink" data-title="KASTU (Finland)">KASTU</a>)<a href="#fn12" class="footnote-ref" id="fnref12" role="doc-noteref"><sup>12</sup></a>, <strong>France</strong> (<a class="maplink" data-title="Deployment of TAJ">TAJ</a>)<a href="#fn13" class="footnote-ref" id="fnref13" role="doc-noteref"><sup>13</sup></a>, <strong>Germany</strong> (<a class="maplink" data-title="German central criminal information system INPOL">INPOL</a>)<a href="#fn14" class="footnote-ref" id="fnref14" role="doc-noteref"><sup>14</sup></a>, <strong>Greece</strong> (<a class="maplink" data-title="Facial Recognition in Greece (Law Enforcement)">Mugshot Database</a>)<a href="#fn15" class="footnote-ref" id="fnref15" role="doc-noteref"><sup>15</sup></a>, <strong>Hungary</strong> (<a class="maplink" data-title="NEC Face Recognition Search Engine in Hungary">Facial Image Registry</a>)<a href="#fn16" class="footnote-ref" id="fnref16" role="doc-noteref"><sup>16</sup></a>, <strong>Italy</strong> (<a class="maplink" data-title="AFIS (Deployment, Italy)">AFIS</a>)<a href="#fn17" class="footnote-ref" id="fnref17" role="doc-noteref"><sup>17</sup></a>, <strong>Latvia</strong> (<a class="maplink" data-title="BDAS Deployment (Latvia)">BDAS</a>)<a href="#fn18" class="footnote-ref" id="fnref18" role="doc-noteref"><sup>18</sup></a>, <strong>Lithuania</strong> (<a class="maplink" data-title="HDR (Deployment, Lithuania)">HDR</a>)<a href="#fn19" class="footnote-ref" id="fnref19" role="doc-noteref"><sup>19</sup></a>, <strong>Netherlands</strong> (<a class="maplink" data-title="CATCH">CATCH</a>)<a href="#fn20" class="footnote-ref" id="fnref20" role="doc-noteref"><sup>20</sup></a> and <strong>Slovenia</strong> (<a class="maplink" data-title="VeriLook (and Face Trace) in Slovenia">Record of Photographed Persons</a>)<a href="#fn21" class="footnote-ref" id="fnref21" role="doc-noteref"><sup>21</sup></a> (TELEFI 2021).</p>
<p><strong>Seven additional countries</strong> are expected to acquire such capabilities in the near future: <strong>Croatia</strong> (<a class="maplink" data-title="ABIS (Deployment, Croatia)">ABIS</a>)<a href="#fn22" class="footnote-ref" id="fnref22" role="doc-noteref"><sup>22</sup></a>, <strong>Czech Republic</strong> (<a class="maplink" data-title="CBIS (deployment, Czech Republic)">CBIS</a>)<a href="#fn23" class="footnote-ref" id="fnref23" role="doc-noteref"><sup>23</sup></a>, <strong>Portugal</strong> (<a class="maplink" data-title="AFIS (Deployment, Portugal)">AFIS</a>) <strong>Romania</strong> (<a class="maplink" data-title="Romanian Police use of Facial Recognition (NBIS)">NBIS</a>)<a href="#fn24" class="footnote-ref" id="fnref24" role="doc-noteref"><sup>24</sup></a>, <strong>Spain</strong> (<a class="maplink" data-title="ABIS (Deployment, Spain)">ABIS</a>), <strong><a class="maplink" data-title="National Forensic Center">Sweden</a></strong> (<a class="maplink" data-title="Facial Recognition National Mugshot Database (Sweden)">National Mugshot Database</a>), <strong>Cyprus</strong> (<a class="maplink" data-title="Facial Recognition in Cyprus (Law Enforcement)">ISIS Faces</a>)<a href="#fn25" class="footnote-ref" id="fnref25" role="doc-noteref"><sup>25</sup></a>, <strong><a class="maplink" data-title="Estonian Forensic Science Institute">Estonia</a></strong> (<a class="maplink" data-title="ABIS (Deployment, Estonia)">ABIS</a>)<a href="#fn26" class="footnote-ref" id="fnref26" role="doc-noteref"><sup>26</sup></a> (TELEFI 2021).</p>
<p>When it comes to international institutions, <strong><a class="maplink" data-title="Interpol">Interpol</a></strong> (2020) has a facial recognition system (<a class="maplink" data-title="IFRS (Interpol)">IFRS</a>)<a href="#fn27" class="footnote-ref" id="fnref27" role="doc-noteref"><sup>27</sup></a>, based on facial images received from more than 160 countries. <strong><a class="maplink" data-title="Europol">Europol</a></strong> has two sub-units which use the facial recognition search tool and database known as <a class="maplink" data-title="FACE Deployment by EUROPOL">FACE</a>: the European Counter Terrorism Center (ECTC) and the European Cybercrime Center (ECC). (TELEFI, 2021 149-153) (Europol 2020)</p>
<p><strong>Only 9 countries in the EU so far have rejected or do not plan to implement</strong> FRT for forensic purposes: <strong>Belgium</strong> (see CHAPTER 6), <strong>Bulgaria</strong>, <strong>Denmark</strong>, <strong>Ireland</strong>, <strong>Luxembourg</strong>, <strong>Malta</strong>, <strong>Poland</strong>, <strong>Portugal</strong>, <strong>Slovakia</strong>.</p>
<p><img src="images/media/image1.png" style="width:4.62502in;height:3.28283in" alt="Map Description automatically generated" /></p>
<p>Figure 1. EU Countries use of FRT for forensic applications<a href="#fn25" class="footnote-ref" id="fnref25" role="doc-noteref"><sup>25</sup></a></p>
<p>Figure 1. EU Countries use of FRT for forensic applications<a href="#fn28" class="footnote-ref" id="fnref28" role="doc-noteref"><sup>28</sup></a></p>
<p><strong>When it comes to databases</strong>, some countries limit the searches to <strong>criminal databases</strong> (Austria, Germany, France, Italy, Greece, Slovenia, Lithuania, UK), while other countries open the searches to <strong>civil databases</strong> (Finland, Netherlands, Latvia, Hungary).</p>
<p>This means that the <strong>person categories can vary substantially.</strong> In the case of criminal databases it can range from suspects and convicts, to asylum seekers, aliens, unidentified persons, immigrants, visa applicants. When <strong>civil databases</strong> are used as well, such as in Hungary, the database contains a broad range of “individuals of known identity from various document/civil proceedings” (TELEFI 2021, appendix 3).</p>
<p><strong>Finally, the database sizes</strong>, in comparison to the authentication databases mentioned in the previous section, are of a different magnitude. The databases of school students in France and Sweden, mentioned in the previous section contains a few hundred entries. National databases can contain instead several millions. Criminal databases such as Germany’s INPOL contains <strong>6,2 million individuals</strong>, France’s <a class="maplink" data-title="Deployment of TAJ">TAJ</a> <strong>21 million individuals</strong> and <a class="maplink" data-title="AFIS (Deployment, Italy)">Italy’s AFIS</a> <strong>9 million individuals.</strong> Civil databases, such as Hungary’s Facial Image Registry contain <strong>30 million templates</strong> (TELEFI, 2021 appendix 3).</p>
@ -899,7 +982,7 @@
</section>
<section id="case-study-inpol-germany" class="level3">
<h3>Case study: INPOL (Germany)</h3>
<p>In order to give a concrete example of the forensic use of biometric technology, we can take the German case. Germany has been using <strong>automated facial recognition</strong> technologies to identify criminal activity since 2008 using a central criminal information system called <strong><a class="maplink" data-title="German central criminal information system INPOL">INPOL</a> (Informationssystem Polizei)</strong>, maintained by the <strong>Bundeskriminalamt (BKA)</strong>, which is the federal criminal police office. INPOL uses <strong>Oracle Software</strong> and includes the following information: name, aliases, date and place of birth, nationality, fingerprints, mugshots, appearance, information about criminal histories such as prison sentences or violence of an individual, and DNA information. However, DNA information is not automatically recorded (TELEFI 2021).</p>
<p>In order to give a concrete example of the forensic use of biometric technology, we can take the German case. Germany has been using <strong>automated facial recognition</strong> technologies to identify criminal activity since 2008 using a central criminal information system called <strong><a class="maplink" data-title="German central criminal information system INPOL">INPOL</a> (Informationssystem Polizei)</strong>, maintained by the <strong><a class="maplink" data-title="German Federal Criminal Police Office (Bundeskriminalamt)">Bundeskriminalamt</a> (BKA)</strong>, which is the federal criminal police office. INPOL uses <strong><a class="maplink" data-title="Oracle Corporation">Oracle Software</a></strong> and includes the following information: name, aliases, date and place of birth, nationality, fingerprints, mugshots, appearance, information about criminal histories such as prison sentences or violence of an individual, and DNA information. However, DNA information is not automatically recorded (TELEFI 2021).</p>
<p>The <a class="maplink" data-title="German central criminal information system INPOL">INPOL</a> database includes <strong>facial images of suspects, arrestees, missing persons, and convicted individuals</strong>. For the purpose of facial recognition, anatomical features of a person's face or head as seen on video surveillance or images are used as a material to match with data in <a class="maplink" data-title="German central criminal information system INPOL">INPOL</a>. The facial recognition system compares templates and lists all the matches ordered by degree of accordance. The BKA has specific personnel visually analysing the system's choices and providing an assessment, defining the probability of identifying a person. This assessment can be used in a court of law if necessary (Bundeskriminalamt, n.d.). Searches in the database are conducted by using <a class="maplink" data-title="Cognitec Systems">Cognitec</a> Face VACS software (TELEFI 2021).</p>
<p>As of March 2020, <strong><a class="maplink" data-title="German central criminal information system INPOL">INPOL</a></strong> consists of <strong>5,8 million images of about 3,6 million individuals</strong>. All police stations in Germany have access to this database. The BKA saves biometric data and can be used by other ministries as well, for instance, to identify asylum seekers. Furthermore, the data is shared in the context of the <strong>Prüm cooperation</strong> on an international level (mostly fingerprints and DNA patterns). Furthermore, the <strong>BKA</strong> saves <strong>DNA analysis data as part of <a class="maplink" data-title="German central criminal information system INPOL">INPOL</a></strong>, accessible for all police stations in Germany. That database contains <strong>1,2 million data sets</strong> (Bundeskriminalamt, n.d.). Other recorded facial images, for instance, driver’s licenses or passports, are not included in the search, and the database is mainly used for police work (TELEFI 2021).</p>
</section>
@ -914,13 +997,13 @@
<p>A second broad use of image and audio-based security technologies is for surveillance purposes. Here again, it is important, we suggest, to distinguish between two broad categories.</p>
<section id="smart-surveillance-features" class="level3">
<h3>Smart surveillance features</h3>
<p>A first range of deployments of <strong>“smart” systems</strong> correspond to what can broadly be defined as “smart surveillance” yet <strong>do not collect or process biometric information per se</strong><a href="#fn26" class="footnote-ref" id="fnref26" role="doc-noteref"><sup>26</sup></a>. Smart systems can be used <strong>ex-post</strong>, <strong>to assist CCTV camera operators</strong> in processing large amounts of <strong>recorded information</strong>, or can guide their attention when they have to monitor a large number of <strong>live video feeds</strong> simultaneously. Smart surveillance uses the following features:</p>
<p><strong>- Anomaly detection. In Toulouse (France), the City Council commissioned <a class="maplink" data-title="IBM">IBM</a> to connect 30 video surveillance cameras to software able to "assist human decisions" by raising alerts when "abnormal events are detected." (Technopolice 2021) The request was justified by the “difficulties of processing the images generated daily by the 350 cameras and kept for 30 days (more than 10,000 images per second)”. The objective, according to the digital direction is "to optimise and structure the supervision of video surveillance operators by generating alerts through a system of intelligent analysis that facilitates the identification of anomalies detected, whether: movements of crowds, isolated luggage, crossing virtual barriers north of the Garonne, precipitous movement, research of shapes and colour. All these detections are done in real time or delayed (Technopolice 2021). In other words, the anomaly detection is a way to <em>operationalise</em> the numerical output of various computer vision based recognition systems. Similar systems are used</strong> in the <strong>Smart video surveillance deployment in Valenciennes (France)</strong> or in the <strong>Urban Surveillance Centre (Marseille).</strong></p>
<p><strong>- Object Detection.</strong> In Amsterdam, around the <strong><a class="maplink" data-title="Johan Cruijff ArenA">Johan Cruijff ArenA</a></strong> (Stadium), the city has been experimenting with a <strong><a class="maplink" data-title="Digitale Perimeter">Digitale Perimeter</a></strong> (digital perimeter) surveillance system. In addition to the usual features of facial recognition, and crowd monitorining, the system includes the possibility of automatically detecting specific objects such as <strong>weapons, fireworks</strong> or <strong>drones</strong>. Similar features are found in <strong><a class="maplink" data-title="Inwebit">Inwebit</a>’s Smart Security Platform (SSP) in Poland.</strong></p>
<p><strong>- Feature search. In <a class="maplink" data-title="City of Marbella">Marbella</a> (Spain), <a class="maplink" data-title="Avigilon">Avigilon</a> deployed <a class="maplink" data-title="Avigilon deployment in Marbella">a smart camera system</a> aimed at providing “smart” functionalities without biometric data. Since regional law bans facial and biometric identification without consent, the software uses “appearance search”. “Appearance search” provides estimates for “unique facial traits, the colour of a person’s clothes, age, shape, gender and hair colour”. This information is not considered biometric. The individual’s features can be used to search for suspects fitting a particular profile. Similar technology has been deployed in Kortrijk (Belgium), which provides search parameters for people, vehicles and animals (</strong>Verbeke 2019)<strong>.</strong></p>
<p>- <strong>Video summary.</strong> Some companies, such as <strong><a class="maplink" data-title="Briefcam Ltd">Briefcam</a></strong> and their product <strong>Briefcam Review</strong>, offer a related product, which promises to shorten the analysis of long hours of CCTV footage, by identifying specific topics of interest (children, women, lighting changes) and making the footage searchable. The product combines face recognition, license plate recognition, and more mundane video analysis features such as the possibility to overlay selected scenes, thus highlighting recurrent points of activity in the image. Briefcam is deployed in several cities across Europe, including <a class="maplink" data-title="Briefcam deployment in Vannes">Vannes</a>, <a class="maplink" data-title="Center of Urban Supervision (Roubaix)">Roubaix</a> (in partnership with <strong><a class="maplink" data-title="Eiffage">Eiffage</a></strong>) and <a class="maplink" data-title="City of Moirans">Moirand</a> in France.</p>
<p><strong>- Object detection and object tracking. As outlined in chapter 2, object detection is often the first step in the various digital detection applications for images. An ‘object’ here can mean anything the computer is conditioned to search for: a suitcase, a vehicle, but also a person; while some products further process the detected object to estimate particular features, such as the colour of a vehicle, the age of a person. However, on some occasions — often to address concerns over privacy — only the position of the object on the image is stored. This is for example the case with the</strong> test of the <strong><a class="maplink" data-title="Test of One and a half meter monitor">One-and-a-half-meter monitor</a> in Amsterdam (Netherlands), <a class="maplink" data-title="Intemo">Intemo</a>’s people counting system in Nijmegen (Netherlands),</strong> the <strong><a class="maplink" data-title="Project KICK">KICK project</a></strong> in <strong><a class="maplink" data-title="Brugge Municipality">Brugge</a></strong>, <strong><a class="maplink" data-title="Kortrijk Municipality">Kortrijk</a></strong>, <strong><a class="maplink" data-title="Kortrijk Municipality">Ieper</a></strong>, <strong><a class="maplink" data-title="Roeselare Municipality">Roeselare</a></strong> and <strong><a class="maplink" data-title="Economisch Huis Oostende">Oostende</a></strong> in Belgium or the <strong><a class="maplink" data-title="Eco-Counter">Eco-counter</a></strong> <strong><a class="maplink" data-title="Tracking cameras pilot in Lannion">tracking cameras pilot project</a></strong> in <strong><a class="maplink" data-title="City of Lannion">Lannion</a></strong> (France).</p>
<p><strong>- Movement recognition. <a class="maplink" data-title="Avigilon">Avigilon</a>’s software that is deployed in Marbella (Spain) also detects unusual movement. “To avoid graffiti, we can calculate the time someone takes to pass a shop window, “explained Javier Martín, local chief of police in Marbella to the Spanish newspaper El País. “If it takes them more than 10 seconds, the camera is activated to see if they are graffitiing. So far, it hasn’t been activated.” (Colomé 2019) Similar movement recognition technology is used in, the ViSense deployment at the Olympic Park London (UK) and the security camera system in Mechelen-Willebroek (Belgium). It should be noted that movement</strong> recognition can be done in two ways: where projects such as the <strong><a class="maplink" data-title="Data-lab Burglary-free Neighbourhood">Data-lab Burglary-free Neighbourhood</a> in Rotterdam (Netherlands)</strong><a href="#fn27" class="footnote-ref" id="fnref27" role="doc-noteref"><sup>27</sup></a> are only based on the tracking of trajectories of people through an image (see also ‘Object detection’), cases such as <strong>the <a class="maplink" data-title="Living Lab Stratumseind">Living Lab Stratumseind</a></strong><a href="#fn28" class="footnote-ref" id="fnref28" role="doc-noteref"><sup>28</sup></a> <strong>in Eindhoven (Netherlands)</strong> also process the movements and gestures of individuals in order to estimate their behaviour.</p>
<p>A first range of deployments of <strong>“smart” systems</strong> correspond to what can broadly be defined as “smart surveillance” yet <strong>do not collect or process biometric information per se</strong><a href="#fn29" class="footnote-ref" id="fnref29" role="doc-noteref"><sup>29</sup></a>. Smart systems can be used <strong>ex-post</strong>, <strong>to assist CCTV camera operators</strong> in processing large amounts of <strong>recorded information</strong>, or can guide their attention when they have to monitor a large number of <strong>live video feeds</strong> simultaneously. Smart surveillance uses the following features:</p>
<p><strong>- Anomaly detection. <a class="maplink" data-title="IBM Smart CCTV deployment in Toulouse">In Toulouse</a> (France), the City Council commissioned <a class="maplink" data-title="IBM">IBM</a> to connect 30 video surveillance cameras to software able to "assist human decisions" by raising alerts when "abnormal events are detected." (Technopolice 2021) The request was justified by the “difficulties of processing the images generated daily by the 350 cameras and kept for 30 days (more than 10,000 images per second)”. The objective, according to the digital direction is "to optimise and structure the supervision of video surveillance operators by generating alerts through a system of intelligent analysis that facilitates the identification of anomalies detected, whether: movements of crowds, isolated luggage, crossing virtual barriers north of the Garonne, precipitous movement, research of shapes and colour. All these detections are done in real time or delayed (Technopolice 2021). In other words, the anomaly detection is a way to <em>operationalise</em> the numerical output of various computer vision based recognition systems. Similar systems are used</strong> in the <strong>Smart video surveillance deployment in Valenciennes (France)</strong> or in the <strong>Urban Surveillance Centre (Marseille).</strong></p>
<p><strong>- Object Detection.</strong> In Amsterdam, around the <strong><a class="maplink" data-title="Johan Cruijff ArenA">Johan Cruijff ArenA</a></strong> (Stadium), the city has been experimenting with a <strong><a class="maplink" data-title="Digitale Perimeter">Digitale Perimeter</a></strong> (digital perimeter) surveillance system. In addition to the usual features of facial recognition, and crowd monitorining, the system includes the possibility of automatically detecting specific objects such as <strong>weapons, fireworks</strong> or <strong>drones</strong>. Similar features are found in <strong><a class="maplink" data-title="Inwebit">Inwebit</a>’s <a class="maplink" data-title="Smart Security Platform (SSP), Poland">Smart Security Platform</a> (SSP) in Poland.</strong></p>
<p><strong>- Feature search. In <a class="maplink" data-title="City of Marbella">Marbella</a> (Spain), <a class="maplink" data-title="Avigilon">Avigilon</a> deployed <a class="maplink" data-title="Avigilon deployment in Marbella">a smart camera system</a> aimed at providing “smart” functionalities without biometric data. Since regional law bans facial and biometric identification without consent, the software uses “appearance search”. “Appearance search” provides estimates for “unique facial traits, the colour of a person’s clothes, age, shape, gender and hair colour”. This information is not considered biometric. The individual’s features can be used to search for suspects fitting a particular profile. Similar technology has been <a class="maplink" data-title="Monitoring Kortrijk">deployed in Kortrijk</a> (Belgium), which provides search parameters for people, vehicles and animals</strong> (Verbeke 2019). <strong>During the Covid-19 pandemic, several initiatives emerged to automatically detect whether the mask mandates were observed by the public, such as in the <a class="maplink" data-title="Face mask recognition in Châtelet-Les Halles (Stopped)">aborted face mask recognition project in Châtelet-Les Halles</a> developed by the company <a class="maplink" data-title="Datakalab">Datakalab</a>.</strong></p>
<p>- <strong>Video summary.</strong> Some companies, such as <strong><a class="maplink" data-title="Briefcam Ltd">Briefcam</a></strong> and their product <strong>Briefcam Review</strong>, offer a related product, which promises to shorten the analysis of long hours of CCTV footage, by identifying specific topics of interest (children, women, lighting changes) and making the footage searchable. The product combines face recognition, license plate recognition, and more mundane video analysis features such as the possibility to overlay selected scenes, thus highlighting recurrent points of activity in the image. Briefcam is deployed in several cities across Europe, including <a class="maplink" data-title="Briefcam deployment in Vannes">Vannes</a>, <a class="maplink" data-title="Eiffage / Briefcam deployment in Roubaix">Roubaix</a> (in partnership with <strong><a class="maplink" data-title="Eiffage">Eiffage</a></strong> managed by <strong>the <a class="maplink" data-title="City of Roubaix">City of Roubaix</a></strong> and the <strong><a class="maplink" data-title="Métropole Européenne de Lille">Métropole Européenne de Lille</a></strong>) and <a class="maplink" data-title="Smart Surveillance in Moirans">Moirans</a> in France (with equipment provided by <strong><a class="maplink" data-title="Nomadys">Nomadys</a></strong>).</p>
<p><strong>- Object detection and object tracking. As outlined in chapter 2, object detection is often the first step in the various digital detection applications for images. An ‘object’ here can mean anything the computer is conditioned to search for: a suitcase, a vehicle, but also a person; while some products further process the detected object to estimate particular features, such as the colour of a vehicle, the age of a person. However, on some occasions — often to address concerns over privacy — only the position of the object on the image is stored. This is for example the case with the</strong> test of the <strong><a class="maplink" data-title="Test of One and a half meter monitor">One-and-a-half-meter monitor</a> in Amsterdam (Netherlands), <a class="maplink" data-title="Intemo">Intemo</a>’s <a class="maplink" data-title="People counting in Nijmegen">people counting system in Nijmegen</a> (Netherlands),</strong> the <a class="maplink" data-title="ViSense at MINDBase">ViSense social distancing monitor</a> at MINDBase,</strong> a testing location of the <strong><a class="maplink" data-title="Dutch Defence Equipment Organisation">Dutch Defence Equipment Organization</a></strong>; the <strong><a class="maplink" data-title="Project KICK">KICK project</a></strong> in <strong><a class="maplink" data-title="Brugge Municipality">Brugge</a></strong>, <strong><a class="maplink" data-title="Kortrijk Municipality">Kortrijk</a></strong>, <strong><a class="maplink" data-title="Ieper Municipality">Ieper</a></strong>, <strong><a class="maplink" data-title="Roeselare Municipality">Roeselare</a></strong> and <strong><a class="maplink" data-title="Economisch Huis Oostende">Oostende</a></strong> <strong>(Belgium), the <a class="maplink" data-title="ViSense - Mechelen">ViSense project in Mechelen</a> (Belgium)</strong> or the <strong><a class="maplink" data-title="Eco-Counter">Eco-counter</a></strong> <strong><a class="maplink" data-title="Tracking cameras pilot in Lannion">tracking cameras pilot project</a></strong> in <strong><a class="maplink" data-title="City of Lannion">Lannion</a> (France).</strong></p>
<p><strong>- Movement recognition. <a class="maplink" data-title="Avigilon">Avigilon</a>’s software that is deployed in Marbella (Spain) also detects unusual movement. “To avoid graffiti, we can calculate the time someone takes to pass a shop window, “explained Javier Martín, local chief of police in Marbella to the Spanish newspaper El País. “If it takes them more than 10 seconds, the camera is activated to see if they are graffitiing. So far, it hasn’t been activated.” (Colomé 2019) Similar movement recognition technology is used in, the ViSense deployment at the Olympic Park London (UK) and the <a class="maplink" data-title="Security cameras Mechelen-Willebroek">security camera system in Mechelen-Willebroek</a> (Belgium). It should be noted that movement</strong> recognition can be done in two ways: where projects such as the <strong><a class="maplink" data-title="Data-lab Burglary-free Neighbourhood">Data-lab Burglary-free Neighbourhood</a> in Rotterdam (Netherlands)</strong><a href="#fn30" class="footnote-ref" id="fnref30" role="doc-noteref"><sup>30</sup></a> are only based on the tracking of trajectories of people through an image (see also ‘Object detection’), cases such as <strong>the <a class="maplink" data-title="Living Lab Stratumseind">Living Lab Stratumseind</a></strong><a href="#fn31" class="footnote-ref" id="fnref31" role="doc-noteref"><sup>31</sup></a> <strong>in Eindhoven (Netherlands)</strong> also process the movements and gestures of individuals in order to estimate their behaviour.</p>
<section id="audio-recognition-1" class="level4">
<h4>Audio recognition</h4>
<p>- In addition to image (video) based products, some deployments use audio recognition to complement the decision-making process, for example used in the <strong><a class="maplink" data-title="Serenecity">Serenecity</a> (a branch of <a class="maplink" data-title="Verney-Carron SA">Verney-Carron</a>) Project in Saint-Etienne (France)</strong>, the <strong><a class="maplink" data-title="Smart CCTV with audio detection in Rouen public transportation">Smart CCTV deployment in public transportation</a> in <a class="maplink" data-title="City of Rouen">Rouen</a> (France)</strong> or the <strong><a class="maplink" data-title="Smart surveillance coupled with audio recognition (Strasbourg)">Smart CCTV system in Strasbourg</a> (France)</strong>. The <a class="maplink" data-title="Serenicity project (Saint-Etienne)">project piloted in Saint-Etienne</a> for example, worked by placing “audio capture devices” - the term microphone was avoided- in strategic parts of the city. Sounds qualified by an anomaly detection algorithm as suspicious would then alert operators in the Urban Supervision Center, prompting further investigation via CCTV or deployment of the necessary services (healthcare or police for example) (France 3 Auvergne-Rhône-Alpes 2019.)</p>
@ -938,8 +1021,8 @@
<h3>Integrated solutions </h3>
<section id="smart-cities" class="level4">
<h4>Smart cities</h4>
<p>While some cities or companies decide to implement some of the functionalities with their existing or updated CCTV systems, several chose to centralise several of these “smart” functions in <strong>integrated systems</strong> often referred to as “safe city” solutions. These solutions do not necessarily process biometric information. This is the case for example for the deployments in <strong>TIM’s</strong>, <strong>Insula</strong> and <strong>Venis’</strong> <strong><a class="maplink" data-title="Control Room (Venice)">Safe City Platform in Venice</a> (Italy)</strong>, <strong><a class="maplink" data-title="Huawei">Huawei</a>’s</strong> <strong><a class="maplink" data-title="Smart video surveillance in Valenciennes">Safe City in Valenciennes</a> (France)</strong>, <strong><a class="maplink" data-title="Dahua Deployment in Brienon-sur-Armançon">Dahua’s integrated solution in Brienon-sur-Armançon</a></strong> <strong>(France)</strong>, <strong><a class="maplink" data-title="Thales">Thalès</a>’ Safe City in <a class="maplink" data-title="Safe City Pilot (La Défense)">La Défense</a> and <a class="maplink" data-title= "Safe City Pilot Project (Nice)",
>Nice</a> (France)</strong>, <strong>Engie Inéo’s and SNEF’s <a class="maplink" data-title="SNEF Smart CCTVs in Marseille">integrated solution in Marseille</a> (France)</strong>, the <strong><a class="maplink" data-title="Center of Urban Supervision (Roubaix)">Center of Urban Supervision in Roubaix</a> (France)</strong>, <strong><a class="maplink" data-title="AI Mars (Potential)">AI Mars</a> (Madrid, in development)</strong> or <strong>NEC’s platform in <a class="maplink" data-title="NEC Technology in Lisbon">Lisbon</a> and London</strong>.</p>
<p>While some cities or companies decide to implement some of the functionalities with their existing or updated CCTV systems, several chose to centralise several of these “smart” functions in <strong>integrated systems</strong> often referred to as “safe city” solutions. These solutions do not necessarily process biometric information. This is the case for example for the deployments in <strong><a class="maplink" data-title="Gruppo TIM">TIM</a>’s</strong>, <strong><a class="maplink" data-title="Insula Spa">Insula</a></strong> and <strong><a class="maplink" data-title="Venis Spa">Venis</a>’</strong> <strong><a class="maplink" data-title="Control Room (Venice)">Safe City Platform in Venice</a> (Italy)</strong>, <strong><a class="maplink" data-title="Huawei">Huawei</a>’s</strong> <strong><a class="maplink" data-title="Smart video surveillance in Valenciennes">Safe City in Valenciennes</a> (France)</strong>, <strong><a class="maplink" data-title="Dahua Deployment in Brienon-sur-Armançon">Dahua’s integrated solution in Brienon-sur-Armançon</a></strong> <strong>(France)</strong>, <strong><a class="maplink" data-title="Thales">Thalès</a>’ Safe City in <a class="maplink" data-title="Safe City Pilot (La Défense)">La Défense</a> and <a class="maplink" data-title= "Safe City Pilot Project (Nice)",
>Nice</a> (France)</strong>, <strong>Engie Inéo’s and <a class="maplink" data-title="Groupe SNEF">SNEF</a>’s <a class="maplink" data-title="SNEF Smart CCTVs in Marseille">integrated solution in Marseille</a> (France)</strong>, the <strong><a class="maplink" data-title="Center of Urban Supervision (Roubaix)">Center of Urban Supervision in Roubaix</a> (France)</strong>, <strong><a class="maplink" data-title="AI Mars (Potential)">AI Mars</a> (Madrid, in development)</strong><a href="#fn32" class="footnote-ref" id="fnref32" role="doc-noteref"><sup>32</sup></a> or <strong>NEC’s platform in <a class="maplink" data-title="NEC Technology in Lisbon">Lisbon</a> and London</strong>.</p>
<p>The way “Smart/Safe City” solutions work is well exemplified by the <a class="maplink" data-title="Control Room (Venice)">“Control room” deployed in Venice</a>, connected to an urban surveillance network. The system is composed of a central command and control room which aggregates cloud computing systems, together with smart cameras, artificial intelligence systems, antennas and hundreds of sensors distributed on a widespread network. The idea is to monitor what happens in the lagoon city in real time. The scope of the abilities of the centre is wide-ranging. It promises to: manage events and incoming tourist flows, something particularly relevant to a city which aims to implement a visiting fee for tourists; predict and manage weather events in advance, such as the shifting of tides and high water, by defining alternative routes for transit in the city; indicating to the population in real time the routes to avoid traffic and better manage mobility for time optimisation; improve the management of public safety allowing city agents to intervene in a more timely manner; control and manage water and road traffic, also for sanctioning purposes, through specific video-analysis systems; control the status of parking lots; monitor the environmental and territorial situation; collect, process data and information that allow for the creation of forecasting models and the allocation of resources more efficiently and effectively; bring to life a physical "Smart Control Room" where law enforcement officers train and learn how to read data as well. (LUMI 2020)</p>
</section>
<section id="smartphone-apps" class="level4">
@ -948,7 +1031,7 @@
</section>
<section id="crowd-management" class="level4">
<h4>Crowd management</h4>
<p>Integrated solutions are generally comprised of a set of crowd management features, such as in the case of the systems <strong>in <a class="maplink" data-title="Smart video surveillance in Valenciennes">Valenciennes</a> and <a class="maplink" data-title="Urban Surveillance Center in Marseille">Marseille</a> (France), <a class="maplink" data-title="Mannheim public surveillance">Mannheim</a> (Germany), <a class="maplink" data-title="Control Room (Venice)">Venice</a> (Italy), Amsterdam, <a class="maplink" data-title="Citybeacons Eindhoven">Eindhoven</a> and Den Bosch with the <a class="maplink" data-title="CrowdWatch">Crowdwatch</a> project (Netherlands).</strong> Such crowd management software generally does not recognise individuals, but rather estimates the number of people on (a part of) the video frame. Sudden movements of groups or changes in density are then flagged for attention of the security operator (Nishiyama 2018).</p>
<p>Integrated solutions are generally comprised of a set of crowd management features, such as in the case of the systems <strong>in <a class="maplink" data-title="Smart video surveillance in Valenciennes">Valenciennes</a> and <a class="maplink" data-title="Urban Surveillance Center in Marseille">Marseille</a> (France), <a class="maplink" data-title="Mannheim public surveillance">Mannheim</a> (Germany), <a class="maplink" data-title="Control Room (Venice)">Venice</a> (Italy), Amsterdam, <a class="maplink" data-title="Citybeacons Eindhoven">Eindhoven</a> and Den Bosch with the <a class="maplink" data-title="Korte Putstraat (Stopped)">pilot in the Korte Putstraat</a> (using software by <a class="maplink" data-title="CrowdWatch">CrowdWatch</a>, Netherlands).</strong> Such crowd management software generally does not recognise individuals, but rather estimates the number of people on (a part of) the video frame. Sudden movements of groups or changes in density are then flagged for attention of the security operator (Nishiyama 2018).</p>
</section>
</section>
</section>
@ -962,10 +1045,8 @@
<p>- <strong>Live Facial Recognition in Budapest</strong> (Hungary, see detailed case study, CHAPTER 10)</p>
<p>- <strong>Live Facial Recognition <a class="maplink" data-title="Facial Recognition Pilot Project during Carnival (Nice)">pilot project during the Carnival in Nice</a></strong> (France, see detailed case study, CHAPTER 8)</p>
<p>- <strong>Live Facial Recognition <a class="maplink" data-title="Pilot Project Südkreuz Berlin">Pilot Project Südkreuz Berlin</a></strong> (Germany, see detailed case study, CHAPTER 9)</p>
<ul>
<li><p>Live Facial Recognition during <a class="maplink" data-title="Korte Putstraat (Stopped)">Carnival 2019 in 's-Hertogenbosch’s Korte Putstraat</a> (the (Netherlands)</p></li>
</ul>
<p>As most of these cases are extensively discussed in the following chapters, we do not comment further on them here.</p>
<p>Additional cases are the <strong><a class="maplink" data-title="Korte Putstraat (Stopped)"></a>Live Facial Recognition pilot during Carnival 2019</a> in 's-Hertogenbosch’s Korte Putstraat</strong> (the Netherlands) and the pilot of <strong><a class="maplink" data-title="SARI Enterprise in Como">Live Facial Recognition in the city of Como</a></strong><a href="#fn33" class="footnote-ref" id="fnref33" role="doc-noteref"><sup>33</sup></a>, recently struck down by the Italian DPA (<a class="maplink" data-title="Garante per la Privacy">Garante per la Privacy</a>). The deployment of facial recognition in <strong><a class="maplink" data-title="Madrid Estacion Sur">Estacion Sur</a></strong> in Madrid (Spain) is also live.</p>
</section>
<section id="deployment-of-rbi-in-commercial-spaces" class="level3">
<h3>Deployment of RBI in commercial spaces</h3>
@ -1095,7 +1176,7 @@
<h3>Active promotion</h3>
<p>A certain number of actors, both at the national and at the local level are pushing for the development and the extension of biometric remote identification. At the local level, the new technological developments meet a growing apetite for smart city initiatives and the ambitions of mayors that strive for developing digital platforms and employ technology-oriented solutions for governance and law enforcement. The intention of the mayor of Nice, Christian Etrosi, to make <strong>Nice a “laboratory” of crime prevention, despite repeated concerns of the French DPA,</strong> is a case in point (for a detailed analysis, see chapter 8 in this report, see also Barelli 2018). Law enforcement agencies across Europe also continue to press ahead with efforts to build <strong>digital and automated infrastructures that benefits tech companies who push their face recognition technologies with the concept of smart city and innovation tech</strong> (ex. <a class="maplink" data-title="Huawei">Huawei</a>, <a class="maplink" data-title="NEC">NEC</a>, etc.).</p>
<p><strong>At the national level, Biometric systems for the purposes of authentication are increasingly deployed for forensic applications</strong> among law-enforcement agencies in the European Union. As we elaborate in Chapter 3, 11 out of 27 member states of the European Union are already using facial recognition against biometric databases for forensic purposes and 7 additional countries are expected to acquire such capabilities in the near future. The map of the European deployments of Biometric Identification Technologies (see Chapter 3) bear witness to a <strong>broad range of algorithmic processing of security images</strong> in a spectrum that goes from individual, localised authentication systems to generalised law enforcement uses of authentication, to Biometric Mass Surveillance.</p>
<p>Several states that have not yet adopted such technologies seem inclined to follow the trend, and push further. Belgian Minister of Interior Pieter De Crem for example, recently declared he was in favour of the use of facial recognition both for judicial inquiries but also for live facial recognition, a much rarer instance.</p>
<p>Several states that have not yet adopted such technologies seem inclined to follow the trend, and push further. Former Belgian Minister of Interior Pieter De Crem for example, recently declared he was in favour of the use of facial recognition both for judicial inquiries but also for live facial recognition, a much rarer instance.</p>
<section id="the-use-of-facial-recognition-can-mean-increased-efficiency-for-security-services-the-police-are-interested-in-using-this-technology-in-several-of-their-missions.-first-of-all-within-the-framework-of-the-administrative-police-with-the-aim-of-guaranteeing-the-security-of-a-closed-place-accessible-to-the-public-it-would-allow-them-to-immediately-intercept-a-person-who-is-known-in-the-police-databases-and-who-constitutes-a-danger-for-public-security-but-this-technology-can-also-be-used-within-the-framework-of-the-judicial-police-with-the-aim-of-controlling-during-an-investigation-if-the-suspect-was-present-at-the-scene-of-the-crime-at-the-time-when-the-punishable-act-was-committed.-de-halleux-2020" class="level4 Quote">
<blockquote class="Quote">"The use of facial recognition can mean increased efficiency for security services […] The police are interested in using this technology in several of their missions. First of all, within the framework of the administrative police, with the aim of guaranteeing the security of a closed place accessible to the public, it would allow them to immediately intercept a person who is known in the police databases and who constitutes a danger for public security; but this technology can also be used within the framework of the judicial police, with the aim of controlling, during an investigation, if the suspect was present at the scene of the crime at the time when the punishable act was committed". <footer>(De Halleux 2020)</footer></blockquote>
<p>Such outspoken advocates of the use of RBI constitute an important voice, but do not find an echo in the EU mainstream discussions.</p>
@ -1123,7 +1204,7 @@
</section>
<section id="outright-ban" class="level3">
<h3>Outright Ban</h3>
<p>Finally, a certain number of EU Political Parties, EU and national NGOs have argued that there is no acceptable deployment of RBI, because the danger of Biometric Mass Surveillance is too high. Such actors include organisations such as EDRi, <a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a>, <a class="maplink" data-title="Algorithm Watch">Algorithm Watch</a> or the French Défenseur des Droits<a href="#fn29" class="footnote-ref" id="fnref29" role="doc-noteref"><sup>29</sup></a>.</p>
<p>Finally, a certain number of EU Political Parties, EU and national NGOs have argued that there is no acceptable deployment of RBI, because the danger of Biometric Mass Surveillance is too high. Such actors include organisations such as EDRi, <a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a>, <a class="maplink" data-title="Algorithm Watch">Algorithm Watch</a> or the French Défenseur des Droits<a href="#fn34" class="footnote-ref" id="fnref34" role="doc-noteref"><sup>34</sup></a>.</p>
<p>In the European Parliament, the <strong>European Greens</strong> have most vocally promoted the position of the ban, and have gathered support across party lines. In a letter to the European Commission dated 15 April 2021, 40 MEPs from the European Greens, the Party of the European Left, the Party of European Socialists, Renew Europe, a few non-attached MEPs and one member of the far-right party Identity and Democracy expressed their concerns about the leaked EU commission’ proposal for the AI Regulation a few days earlier. As they argued</p>
<section id="people-who-constantly-feel-watched-and-under-surveillance-cannot-freely-and-courageously-stand-up-for-their-rights-and-for-a-just-society.-surveillance-distrust-and-fear-risk-gradually-transforming-our-society-into-one-of-uncritical-consumers-who-believe-they-have-nothing-to-hide-and---in-a-vain-attempt-to-achieve-total-security---are-prepared-to-give-up-their-liberties.-that-is-not-a-society-worth-living-in-breyer-et-al.-2021" class="level4 Quote">
<blockquote class="Quote">People who constantly feel watched and under surveillance cannot freely and courageously stand up for their rights and for a just society. Surveillance, distrust and fear risk gradually transforming our society into one of uncritical consumers who believe they have “nothing to hide” and - in a vain attempt to achieve total security - are prepared to give up their liberties. That is not a society worth living in! <footer>(Breyer et al. 2021)</footer></blockquote>
@ -1185,13 +1266,13 @@
</ul>
</div> <!-- key points -->
<p>Belgium is, with Spain, one of the few countries in Europe that <strong>has not authorised the use of facial recognition technology</strong>, neither for criminal investigations nor for mass surveillance (Vazquez 2020). This does not mean that it is unlikely to change its position in the very near future. <strong>Law enforcement is indeed strongly advocating its use</strong>, and the current legal obstacles are not likely to hold for very long (Bensalem 2018). The pilot experiment that took place in Zaventem / Brussels International Airport, although aborted, occurred within a national context in which <strong>biometric systems are increasingly used and deployed</strong>.</p>
<p>Belgium will, for example, soon roll out at the national level the new biometric identity card “<strong>eID</strong>”, the Minister of Interior Annelies Verlinden has recently announced. The identification document, which will rely on the constitution of a broad biometric database and is part of a broader <a class="maplink" data-title="European Union">European Union</a> initiative, is developed in partnership with security multinational <strong><a class="maplink" data-title="Thales">Thales</a></strong>, was already trialled with 53.000 citizens in (Prins 2021; Thales Group 2020).<a href="#fn30" class="footnote-ref" id="fnref30" role="doc-noteref"><sup>30</sup></a></p>
<p>Municipalities in different parts of the country are experimenting with <strong>Automated Number Plate Recognition (ANPR) technology</strong>. A smaller number have started deploying “<strong>smart CCTV</strong>” cameras, which fall just short of using facial recognition technology. The city of Kortrijk has for example deployed “<strong>body recognition</strong>” technology, which uses walking style or clothing of individuals to track them across the city’s CCTV network. Facial recognition is possible with these systems, but has not been activated as of yet <strong>pending legal authorisation to do so</strong>. In the city of Roeselare, “smart cameras” have been installed in one of the shopping streets. Deployed by telecom operator Citymesh, they could provide facial recognition services, but are currently used to count and estimate crowds, data which is shared with the police (van Brakel 2020). All the emerging initiatives of remote biometric identification are however pending a reversal of the decision to halt <a class="maplink" data-title="Facial Recognition in Brussels Airport (Stopped)">the experiment at Zaventem Brussels International Airport</a>.</p>
<p>Belgium will, for example, soon roll out at the national level the new biometric identity card “<strong>eID</strong>”, the Minister of Interior Annelies Verlinden has recently announced. The identification document, which will rely on the constitution of a broad biometric database and is part of a broader <a class="maplink" data-title="European Union">European Union</a> initiative, is developed in partnership with security multinational <strong><a class="maplink" data-title="Thales">Thales</a></strong>, was already trialled with 53.000 citizens in (Prins 2021; Thales Group 2020).<a href="#fn35" class="footnote-ref" id="fnref35" role="doc-noteref"><sup>35</sup></a></p>
<p>Municipalities in different parts of the country are experimenting with <strong>Automated Number Plate Recognition (ANPR) technology</strong>. A smaller number have started deploying “<strong>smart CCTV</strong>” cameras, which fall just short of using facial recognition technology. The city of Kortrijk has for example <a class="maplink" data-title="Monitoring Kortrijk">deployed</a> “<strong>body recognition</strong>” technology, which uses walking style or clothing of individuals to track them across the city’s CCTV network<a href="#fn36" class="footnote-ref" id="fnref36" role="doc-noteref"><sup>36</sup></a>. Facial recognition is possible with these systems, but has not been activated as of yet <strong>pending legal authorisation to do so</strong>. In the city of <a class="maplink" data-title="Roeselare Municipality">Roeselare</a>, “smart cameras” have been installed in one of the shopping streets. Deployed by telecom operator Citymesh, they could provide facial recognition services, but are currently used to count and estimate crowds, data which is shared with the police (van Brakel 2020). All the emerging initiatives of remote biometric identification are however pending a reversal of the decision to halt <a class="maplink" data-title="Facial Recognition in Brussels Airport (Stopped)">the experiment at Zaventem Brussels International Airport</a>.</p>
<section id="the-zaventem-pilot-in-the-context-of-face-recognition-technology-in-belgium" class="level2">
<h2>The Zaventem pilot in the context of Face Recognition Technology in Belgium</h2>
<p>The use of <strong>facial recognition technology</strong> at the Brussels International Airport was announced on 10 July 2019 in the Flemish weekly <em>Knack</em> by General Commissioner of Federal Police Marc De Mesmaeker (Lippens and Vandersmissen 2019). There is currently no publicly available information as to whom provided the technical system. De Mesmaeker explained that an agreement had been found with the company managing the airport and the labour unions, and thus that the technology was already in use (Organe de Controle de l'Information Policière 2019, 3).</p>
<p>As part of the justification for the deployment of FRT in Zaventem, De Mesmaeker made a comparison with <strong>ANPR-enabled cameras</strong>, arguing that “They have already helped to solve investigations quickly, (…). Citizens understand this and have learned to live with their presence, but privacy remains a right”. (7sur7 2019)</p>
<p>The <strong>Belgian Supervisory Body for Police Information (COC)</strong><a href="#fn31" class="footnote-ref" id="fnref31" role="doc-noteref"><sup>31</sup></a>, in its advisory document, explained that it had no prior knowledge of the deployment and learned about the existence of the facial recognition systems through the interview of De Mesmaeker in the <em>Knack</em> magazine (Organe de Controle de l'Information Policière 2019, 3). On 10 July 2019, the COC thus invited the General Commissioner to communicate all the details of the deployment of this technology in the Brussels International Airport. On 18 July 2019, COC received a summary of the system’s main components. On 9 August 2019, it subsequently visited the premises of the <a class="maplink" data-title="Facial Recognition in Brussels Airport (Stopped)">federal police deployment in Zaventem airport</a> (Organe de Controle de l'Information Policière 2019, 3).</p>
<p>The <strong>Belgian Supervisory Body for Police Information (COC)</strong><a href="#fn37" class="footnote-ref" id="fnref37" role="doc-noteref"><sup>37</sup></a>, in its advisory document, explained that it had no prior knowledge of the deployment and learned about the existence of the facial recognition systems through the interview of De Mesmaeker in the <em>Knack</em> magazine (Organe de Controle de l'Information Policière 2019, 3). On 10 July 2019, the COC thus invited the General Commissioner to communicate all the details of the deployment of this technology in the Brussels International Airport. On 18 July 2019, COC received a summary of the system’s main components. On 9 August 2019, it subsequently visited the premises of the <a class="maplink" data-title="Facial Recognition in Brussels Airport (Stopped)">federal police deployment in Zaventem airport</a> (Organe de Controle de l'Information Policière 2019, 3).</p>
<p>We know some technical details about the system through the public information shared by the COC. In early 2017, Brussels airport had acquired <strong>4 cameras connected to a facial recognition software for use by the airport police</strong> (Police Aéronautique, LPA) (Farge 2020, 15; Organe de Controle de l'Information Policière 2019, 3). The system works in two steps.</p>
<p>When provided with video feeds from the four cameras, the software first creates <strong>snapshots</strong>, generating individual records with the faces that appear in the frame. These snapshots on record are then in a second step compared and potentially matched to previously established “<strong>blacklists</strong>” created by the police itself (the reference dataset is thus not external to this particular deployment) (Organe de Controle de l'Information Policière 2019, 3).</p>
<p>The system did however not live up to its promise and generated a high number of <strong>false positives</strong>. Many features such as skin colour, glasses, moustaches, and beards led to false matches. The system was thus partially disconnected in March 2017, and at the time of the visit of the COC, the system was no longer fully in use (Organe de Controle de l'Information Policière 2019, 3). Yet the second step had not been de-activated (matching video feeds against pre-established blacklists of faces), the first function of creating a biometric record of the video feeds was still in place (Organe de Controle de l'Information Policière 2019, 3).</p>
@ -1227,7 +1308,7 @@
<section id="effects-of-the-technologies" class="level2">
<h2>Effects of the technologies</h2>
<p>While the city of Brussels is the location of much EU-level activism, this hasn’t translated yet in an equal mobilisation at the national level – perhaps due to the currently very restrictive legislative position on the matter and the institutional checks and balances described in this chapter – banning de facto the use of such technologies.</p>
<p>The French campaign Technopolice has extended to Belgium and is raising awareness through a diversified strategy based on public forums, cartography of technology and organization of events. The NGO <strong>Ligue des Droits Humains</strong> is a member of the <strong>Reclaim Your Face</strong> campaign, along with 40 other organisations<a href="#fn32" class="footnote-ref" id="fnref32" role="doc-noteref"><sup>32</sup></a>, yet it hasn’t been as active as partner organizations in neighbouring France or Germany.</p>
<p>The French campaign Technopolice has extended to Belgium and is raising awareness through a diversified strategy based on public forums, cartography of technology and organization of events. The NGO <strong>Ligue des Droits Humains</strong> is a member of the <strong>Reclaim Your Face</strong> campaign, along with 40 other organisations<a href="#fn38" class="footnote-ref" id="fnref38" role="doc-noteref"><sup>38</sup></a>, yet it hasn’t been as active as partner organizations in neighbouring France or Germany.</p>
</section>
</section>
<section id="the-burglary-free-neighbourhood-in-rotterdam-netherlands" class="level1 case" data-title="Data-lab Burglary-free Neighbourhood">
@ -1245,14 +1326,14 @@
<p>In October 2019, the Carlo Collodihof, a courtyard in the Rotterdam neighbourhood Lombardijen, was equipped with a new kind of streetlamp. The twelve new luminaires did not just illuminate the streets; they were <strong>fitted with cameras, microphones, speakers, and a computer which was connected to the internet</strong>. They are part of the so called <strong><a class="maplink" data-title="Data-lab Burglary-free Neighbourhood">Fieldlab Burglary Free Neighbourhood</a></strong>: an experiment in the public space with technologies for computer sensing and data processing, aimed at the prevention of break-ins, robberies, and aggression; increasing the chances of catching and increasing a sense of safety for the inhabitants of the neighbourhood ((Redactie Inbraakvrije Wijk 2019; Kokkeler et al. 2020b). The practical nature of a Fieldlab provides a way to examine concretely how the various technologies come together, and how they fit in with existing infrastructures and regulations.</p>
<section id="detection-and-decision-making-in-the-burglary-free-neighbourhood-fieldlab" class="level2">
<h2>Detection and decision-making in the “Burglary free neighbourhood” Fieldlab</h2>
<p>The national programme Burglary Free Neighbourhood was initiated and funded by the <strong>Dutch Ministry of Justice and Security</strong>. It is led by <strong><a class="maplink" data-title="Dutch Institute for Technology Safety and Security (DITSS)">DITSS</a></strong> (Dutch Institute for Technology, Safety &amp; Security), a non-profit organisation, that has been involved in earlier computer sensing projects in the Netherlands – for example in <strong>Stratumseind, Eindhoven</strong> (The Hague Security Delta 2021). Other parties involved include the municipality of Rotterdam, the police –both on a local and national level– the Public Prosecutor’s Office and insurance company <a class="maplink" data-title="Interpolis">Interpolis</a>. Part of the research is carried out by University of Twente, <a class="maplink" data-title="Avans Hogeschool">Avans Hogeschool</a>, the Network Institute of the Vrije Universiteit Amsterdam and the Max Planck Institute for Foreign and International Criminal Law (Freiburg, Germany).</p>
<p>The national programme Burglary Free Neighbourhood was initiated and funded by the <strong>Dutch Ministry of Justice and Security</strong>. It is led by <strong><a class="maplink" data-title="Dutch Institute for Technology Safety and Security (DITSS)">DITSS</a></strong> (Dutch Institute for Technology, Safety &amp; Security), a non-profit organisation, that has been involved in earlier computer sensing projects in the Netherlands – for example in <strong><a class="maplink" data-title="Living Lab Stratumseind">Stratumseind</a>, Eindhoven</strong> (The Hague Security Delta 2021). Other parties involved include the <a class="maplink" data-title="Rotterdam Municipality">municipality of Rotterdam</a>, the <a class="maplink" data-title="Rotterdam Municipality">police</a> –both on a local and national level– the Public Prosecutor’s Office and insurance company <a class="maplink" data-title="Interpolis">Interpolis</a>. Part of the research is carried out by <a class="maplink" data-title="Rotterdam Municipality">University of Twente</a>, <a class="maplink" data-title="Avans Hogeschool">Avans Hogeschool</a>, the Network Institute of the Vrije Universiteit Amsterdam and the <a class="maplink" data-title="Max Planck Institute for the Study of Crime, Security and Law">Max Planck Institute for Foreign and International Criminal Law</a> (Freiburg, Germany).</p>
<p><img src="images/media/image2.jpg" style="width:6.25564in;height:3.51788in" alt="A picture containing roller coaster, ride Description automatically generated" /></p>
<p>Figure 2. Fieldlab in Rotterdam Lombardijen</p>
<p>From a technological perspective, the project has two aims: to <strong>detect suspicious behaviour</strong>, and in turn<strong>, to influence the behaviour of the suspect</strong>. As such, project manager Guido Delver, who agreed to be interviewed for this report, describes the project as being primarily a behavioural experiment (Delver 2021). The twelve luminaires are provided by <a class="maplink" data-title="Sustainder">Sustainder</a> (their Anne series (Sustainder 2021)). The processing of the video and audio is done on the spot by a computer embedded in the luminaire, using software from the Eindhoven based company <strong><a class="maplink" data-title="ViNotion">ViNotion</a></strong> (ViNotion 2020). This software reads the video frames from the camera and estimates the presence and position of people – thereby mapping the coordinates of the video frame to coordinates in the space. It then determines the direction they are facing. <strong>Only these values –position and direction– and no other characteristics nor any images,</strong> are sent over the internet to a datacentre somewhere in the Netherlands, where the position data is stored for further processing (Delver 2021).</p>
<p>Currently, <strong>there is no immediate processing of the position data</strong> to classify behaviour as being suspicious or not. The proposed pipeline consists of two stages: first, an unsupervised machine algorithm for <strong>anomaly (outlier) detection processes the gathered trajectories</strong>, in order to distinguish trajectories that statistically deviate from the norm. As an example, both children playing, as well as burglars making a scouting round through the neighbourhood can potentially produce anomalous trajectories. Secondly, <strong>these anomalous trajectories are judged as being suspicious or not by a computer model</strong> that was trained with human supervision. In the Fieldlab’s first data collection experiment 100.000 trajectories were collected, totalling 20.000.000 data points (Hamada 2020). It turned out however that this was still too few to draw any conclusions about viability of the approach; the big data was still too small (Delver 2021).</p>
<p>Another input for detecting suspicious situations is the <strong>microphone with which some of the streetlamps are equipped</strong>. By recording two frequencies of sound, sounds can be categorised as coming from for example a conversation, shouting, dog barking, or the breaking of glass. The two frequencies recorded provide too little information to distinguish the words in a conversation (Delver 2021).</p>
<p>Aside from experimenting with the automated detection of suspicious behaviour, the Fieldlab experiments with various ways in which the detected situations can be played out. Project manager Guido Delver notes that the aim is not <em>per se</em> to involve the police. Instead, the suspect should be deterred before any crime is committed (Delver 2021). Various strategies are laid out: the yet-to-be-autonomous system can <strong>voice warnings through the speakers</strong> embedded in the streetlamps. Or, in line with the work of DITSS in Eindhoven’s Stratumseind street, the <strong>light intensity or colour of the streetlamps can be changed</strong> (Intelligent Lighting Institute, n.d.). Both strategies are aimed at signalling the subjects that their behaviour is noticed, which generally suffices to have burglars break off their scouting. Another option under consideration is to send a signal to the residents living nearby.</p>
<p>The process of data gathering in the Burglary Free Neighbourhood is quite similar to technologies that are deployed for anonymous people counting. One such application has been developed by <strong><a class="maplink" data-title="Numina">Numina</a></strong> and is deployed in the Dutch city of Nijmegen: individuals are <strong>traced through space and time, but not identified or categorised.</strong> This information is then used to provide statistics about the number of visitors in the city centre (Schouten and Bril 2019). Another Dutch deployment of technologically similar software is the <strong><a class="maplink" data-title="Test of One and a half meter monitor">One-and-a-half-meter monitor</a> developed by the <a class="maplink" data-title="Amsterdam Municipality">municipality of Amsterdam</a>,</strong> which is based on the YOLO5 object detection algorithm and trained on the COCO dataset. This data processing architecture can detect the presence of persons but is incapable of deducing any characteristics (Amsterdam-Amstelland safety region 2020). These implementations show biometrics can be used to detect the presence of people, while refraining from storing these characteristics.</p>
<p>The process of data gathering in the Burglary Free Neighbourhood is quite similar to technologies that are deployed for anonymous people counting. One such application has been developed by <strong><a class="maplink" data-title="Numina">Numina</a></strong> and is <a class="maplink" data-title="People counting in Nijmegen">deployed in the Dutch city of Nijmegen</a>: individuals are <strong>traced through space and time, but not identified or categorised.</strong> This information is then used to provide statistics about the number of visitors in the city centre (Schouten and Bril 2019). Another Dutch deployment of technologically similar software is the <strong><a class="maplink" data-title="Test of One and a half meter monitor">One-and-a-half-meter monitor</a> developed by the <a class="maplink" data-title="Amsterdam Municipality">municipality of Amsterdam</a>,</strong> which is based on the YOLO5 object detection algorithm and trained on the COCO dataset. This data processing architecture can detect the presence of persons but is incapable of deducing any characteristics (Amsterdam-Amstelland safety region 2020). These implementations show biometrics can be used to detect the presence of people, while refraining from storing these characteristics.</p>
<p><img src="images/media/image3.png" style="width:5.35242in;height:3.07738in" alt="Two people holding umbrellas on a street Description automatically generated with low confidence" /></p>
<p>Figure 3. The one-and-a-half-meter monitor developed by the municipality of Amsterdam</p>
</section>
@ -1297,14 +1378,14 @@
<li><p>Several French cities have launched “safe city” projects involving biometric technologies, however Nice is arguably the national leader. The city currently has the highest CCTV coverage of any city in France and has more than double the police agents per capita of the neighbouring city of Marseille.</p></li>
<li><p>Through a series of public-private partnerships the city began a number of initiatives using RBI technologies (including emotion and facial recognition). These technologies were deployed for both authentication and surveillance purposes with some falling into the category of biometric mass surveillance.</p></li>
<li><p>One project which used FRT at a <a class="maplink" data-title="Facial Recognition Pilot in High School (Nice)">high school in Nice</a> and <a class="maplink" data-title="Facial Recognition Pilot in High School (Marseille)">one in Marseille</a> was eventually declared unlawful. The court determined that the required consent could not be obtained due to the power imbalance between the targeted public (students) and the public authority (public educational establishment). This case highlights important issues about the deployment of biometric technologies in public spaces.</p></li>
<li><p>The use of biometric mass surveillance by the mayor of Nice Christian Estrosi has put him on a collision course with the French Data Protection Authority (<a class="maplink" data-title="CNIL">CNIL</a>) as well as human rights/ digital rights organisations (Ligue des Droits de l’Homme, <a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a>). His activities have raised both concern and criticism over the usage of the technologies and their potential impact on the privacy of personal data.</p></li>
<li><p>The use of biometric mass surveillance by the mayor of Nice Christian Estrosi has put him on a collision course with the French Data Protection Authority (<a class="maplink" data-title="CNIL">CNIL</a>) as well as human rights/ digital rights organisations (<a class="maplink" data-title="La Ligue des droits de l'Homme">Ligue des Droits de l’Homme</a>, <a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a>). His activities have raised both concern and criticism over the usage of the technologies and their potential impact on the privacy of personal data.</p></li>
</ul>
</div> <!-- key points -->
<p>Although several French cities such as Paris, Valenciennes or Marseille have launched pilot projects for “safe city” projects involving <strong>biometric technologies (facial, voice, sound recognition),</strong> the city of Nice is perhaps the <strong>national leader in the experimentation with such technologies at a local level</strong> (Nice Premium 2017). The mayor of Nice, Christian Estrosi (Les Républicains Party, right) a prominent political figure on the national political scene, has made clear his intention was to make Nice a “laboratory” of crime prevention (Barelli 2018). Since 2010, more than <strong>1.962 surveillance cameras have been deployed throughout the city</strong>, making it the city with <strong>highest CCTV coverage in France</strong> (27 cameras per square meter). Nice also possesses the most local police in France per inhabitant: 414 agents, for a population of 340.000 (in comparison, the neighbouring city of Marseille has 450 agents for 861.000 inhabitants).</p>
<p>Although several French cities such as Paris, <a class="maplink" data-title="City of Valenciennes">Valenciennes</a> or <a class="maplink" data-title="Mairie de Marseille">Marseille</a> have launched pilot projects for “safe city” projects involving <strong>biometric technologies (facial, voice, sound recognition),</strong> the city of Nice is perhaps the <strong>national leader in the experimentation with such technologies at a local level</strong> (Nice Premium 2017). The mayor of Nice, Christian Estrosi (Les Républicains Party, right) a prominent political figure on the national political scene, has made clear his intention was to make Nice a “laboratory” of crime prevention (Barelli 2018). Since 2010, more than <strong>1.962 surveillance cameras have been deployed throughout the city</strong>, making it the city with <strong>highest CCTV coverage in France</strong> (27 cameras per square meter). Nice also possesses the most local police in France per inhabitant: 414 agents, for a population of 340.000 (in comparison, the neighbouring city of Marseille has 450 agents for 861.000 inhabitants).</p>
<section id="the-various-facets-of-the-safe-city-project-in-nice" class="level2">
<h2>The various facets of the “Safe city” project in Nice</h2>
<p>Nice has experimented with various initiatives related to <strong>remote biometric identification</strong> – many of which fall into the category of biometric mass surveillance. In 2017, Christian Estrosi announced a partnership with the energy company <a class="maplink" data-title="Engie Ineo">Engie Ineo</a> for the development of an Urban Surveillance Centre (Centre de Surveillance Urbain, CSU). Based on a touch-interface technology, it centralises a platform of <strong>real-time data such as traffic accidents, patrol locations, as well as video feeds from CCTV</strong>s on the streets and in public transportation. (Dudebout 2020, 1). The video feeds from the city tramways are connected to an <strong><a class="maplink" data-title="Facial/Emotion Recognition Pilot in Tramway (Nice)">emotion recognition algorithm</a></strong> to flag suspicious situations (Allix 2018).</p>
<p>In June 2018, an additional step was taken with the signing of a partnership agreement with a consortium of companies headed by <a class="maplink" data-title="Thales">Thales</a>, specialised in social network intelligence, geolocation, biometrics and crowd simulation<a href="#fn33" class="footnote-ref" id="fnref33" role="doc-noteref"><sup>33</sup></a> for a <strong>“Safe City” project</strong> (Dudebout 2020, 2). Established for three years (2018-2021) with a budget of EUR 10,9 million, the project is financed by the city council, subsidised in part by <a class="maplink" data-title="Bpifrance">BPI France</a><a href="#fn34" class="footnote-ref" id="fnref34" role="doc-noteref"><sup>34</sup></a>, and supported by the Committee for the Security Industrial Sector, an agency under the tutelage of the Prime Minister’s office<a href="#fn35" class="footnote-ref" id="fnref35" role="doc-noteref"><sup>35</sup></a> (Allix 2018; BPI France 2018)</p>
<p>In June 2018, an additional step was taken with the signing of a partnership agreement with a consortium of companies headed by <a class="maplink" data-title="Thales">Thales</a>, specialised in social network intelligence, geolocation, biometrics and crowd simulation<a href="#fn39" class="footnote-ref" id="fnref39" role="doc-noteref"><sup>39</sup></a> for a <strong>“Safe City” project</strong> (Dudebout 2020, 2). Established for three years (2018-2021) with a budget of EUR 10,9 million, the project is financed by the city council, subsidised in part by <a class="maplink" data-title="Bpifrance">BPI France</a><a href="#fn40" class="footnote-ref" id="fnref40" role="doc-noteref"><sup>40</sup></a>, and supported by the Committee for the Security Industrial Sector, an agency under the tutelage of the Prime Minister’s office<a href="#fn41" class="footnote-ref" id="fnref41" role="doc-noteref"><sup>41</sup></a> (Allix 2018; BPI France 2018)</p>
<p>The first facial recognition test of the Safe city project took place from 16 February to 2 March 2019, during the Nice Carnival. The experiment was a simulation, involving matching faces collected through CCTV footage of the crowd attending the carnival with a fictitious set of databases (lost individuals, wanted individuals, or individuals with restraining orders). The fictitious datasets were constituted by 50 volunteers, recruited mostly among the municipality, who provided their pictures, or were freshly photographed for the test. The system used <strong>live facial recognition software provided by the company Anyvision</strong>. The live feeds were filmed during the carnival. Passers-by (approximately 1000 people were concerned) were informed of the ongoing test and asked to wear a bracelet if they consented to being filmed (Hassani 2019).</p>
<p>A second experiment took the form of a <strong>software application (app) named “Reporty”,</strong> rolled out in January 2018. The app, developed by the Israeli American company <a class="maplink" data-title="Carbyne">Carbyne</a>, allows citizens to be in direct audio and video connection and share geolocation information with the Urban Supervision Centre in order to report any incivility, offense, or crime that they might witness (Barelli 2018).</p>
<p>The third project, involving <strong>facial recognition</strong> was tested in the education context. In February 2019, <strong><a class="maplink" data-title="Facial Recognition Pilot in High School (Nice)">a high school in Nice</a> and <a class="maplink" data-title="Facial Recognition Pilot in High School (Marseille)">a high school in Marseille</a> were fitted with facial recognition technology</strong> at their gates in order to grant or bar access to the premises. The official motivation behind the deployment was to "assist the personnel of the high schools and to fight against identity theft’’ (Dudebout 2020, 3–4).</p>
@ -1330,8 +1411,8 @@
<section id="mobilisations-and-contestations-2" class="level2">
<h2>Mobilisations and contestations</h2>
<p>The political agenda of Nice’s mayor to be at the forefront of biometric mass surveillance technologies in France and possibly in Europe has put him on a collision course with two main actors: <strong>the French Data Protection Authority (<a class="maplink" data-title="CNIL">CNIL</a>) and human rights/digital rights organisations.</strong></p>
<p>The French digital rights organisation <strong><a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a> was quick to highlight the problems raised by the deployment of these technologies in Nice</strong>. “The safe city is the proliferation of tools from the intelligence community, with a logic of massive surveillance, identification of weak signals and suspicious behaviour," commented Félix Tréguer, a Marseilles-based leader of the association <a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a> and member of the campaign Technopolice<a href="#fn36" class="footnote-ref" id="fnref36" role="doc-noteref"><sup>36</sup></a>. “We do not find it reassuring that the municipal police will become the intelligence service of the urban public space and its digital double" (Allix 2018).</p>
<p><strong>The Ligue des Droits de l’Homme emphasised similar points, highlighting the political dangers involved.</strong> As Henri Busquet of the Ligue des Droits de l'Homme in Nice put “improving emergency services and traffic is legitimate, but the generalisation of video surveillance worries us, and scrutinising social networks is not the role of a mayor. Without any safeguards, such a tool cannot demonstrate the necessary neutrality [...] It is potentially a tool for political destruction, which puts opponents and journalists at particular risk” (Allix 2018).</p>
<p>The French digital rights organisation <strong><a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a> was quick to highlight the problems raised by the deployment of these technologies in Nice</strong>. “The safe city is the proliferation of tools from the intelligence community, with a logic of massive surveillance, identification of weak signals and suspicious behaviour," commented Félix Tréguer, a Marseilles-based leader of the association <a class="maplink" data-title="La Quadrature du Net">La Quadrature du Net</a> and member of the campaign Technopolice<a href="#fn42" class="footnote-ref" id="fnref42" role="doc-noteref"><sup>42</sup></a>. “We do not find it reassuring that the municipal police will become the intelligence service of the urban public space and its digital double" (Allix 2018).</p>
<p><strong>The <a class="maplink" data-title="La Ligue des droits de l'Homme">Ligue des Droits de l’Homme</a> emphasised similar points, highlighting the political dangers involved.</strong> As Henri Busquet of the Ligue des Droits de l'Homme in Nice put “improving emergency services and traffic is legitimate, but the generalisation of video surveillance worries us, and scrutinising social networks is not the role of a mayor. Without any safeguards, such a tool cannot demonstrate the necessary neutrality [...] It is potentially a tool for political destruction, which puts opponents and journalists at particular risk” (Allix 2018).</p>
<p>In July 2019, the city of Nice hoped the <a class="maplink" data-title="CNIL">CNIL</a> would provide advice related to its first test experiment during the Carnival. The <a class="maplink" data-title="CNIL">CNIL</a> responded however that not enough information was provided by the municipality for the DPA to assess it. The French DPA pointed out in particular the lack of “quantified elements on the effectiveness of the technical device or the concrete consequences of a possible bias (related to gender, skin colour ...) of the software” (Dudebout 2020, 3).</p>
<p><strong>The launch of the smartphone application “Reporty” was the catalyst for mobilisation in Nice, united under the umbrella organisation “Collectif anti-Reporty"</strong>. The coalition was formed by local representatives from two left-wing parties (Parti Socialiste, Les Insoumis), Tous Citoyens, the <a class="maplink" data-title="CGT Union (Education) Alpes-Maritimes">union CGT</a> and the anti-discrimination NGO MRAP. The coalition appealed to two institutions to block the use of the application: <strong>The Defender of Rights</strong> (Défenseur des Droits) and the French DPA (<a class="maplink" data-title="CNIL">CNIL</a>). The coalition denounced “a risk of generalised denunciation and a serious breach of privacy”, calling to “put an end to the securitarian drift of Christian Estrosi” (Barelli 2018).</p>
<p><strong>On 15 March 2018, the <a class="maplink" data-title="CNIL">CNIL</a> stated that the application was too invasive and did not meet the criteria set out by the legislation</strong>. It did not meet the proportionality test; it failed to fall within the frame of existing law on video-protection due to the integration of private citizens terminals (smartphones) with a security database managed by the police; it was excessively intrusive due to the collection of images and voice of people in the public space and finally it covered a field of offenses that was too broad (CNIL 2018).</p>
@ -1353,8 +1434,8 @@
<p><strong>Key points</strong></p>
<ul>
<li><p>The German federal police, in cooperation with the German railway company, conducted a project called “Sicherheitsbahnhof” at the Berlin railway station Südkreuz in 2017/18, which included 77 video cameras and a video management system.</p></li>
<li><p>The police in Hamburg used facial recognition software <a class="maplink" data-title="Videmo">Videmo</a> 360 during the protests against the G20 summit in 2017. The database includes 100.000 individuals in Hamburg during the G20 summit and whose profiles are saved in the police database. The technology allows for the determination of behaviour, participation in gatherings, preferences, and religious or political engagement</p></li>
<li><p>Sixty-eight cameras were installed by local police on central squares and places in the German city Mannheim to record the patterns of movement of people. In this project, which started in 2018, the software is used to detect conspicuous behaviour.</p></li>
<li><p>The police in Hamburg used facial recognition software <a class="maplink" data-title="Videmo">Videmo</a> 360 during the protests against the G20 summit in 2017. The database includes 100.000 individuals <a class="maplink" data-title="Facial Recognition during Hamburg G20 Summit">in Hamburg during the G20 summit</a> and whose profiles are saved in the police database. The technology allows for the determination of behaviour, participation in gatherings, preferences, and religious or political engagement</p></li>
<li><p>Sixty-eight cameras were installed by local police on central squares and places in the German city <a class="maplink" data-title="Mannheim Police">Mannheim</a> to record the patterns of movement of people. In this project, which started in 2018, the software is used to detect conspicuous behaviour.</p></li>
<li><p>Half of these deployments (Mannheim &amp; Berlin Südkreuz) took place as measures to test the effectiveness of facial recognition and behavioural analysis software. This “justification as a test” approach is often used in Germany to argue for a deviation from existing rules and societal expectations and was similarly applied during deviations to commonly agreed measures in the Coronavirus/COVID-19 pandemic.</p></li>
<li><p>Resistance to video surveillance is also in no small part a result of constant campaigning and protest by German civil society. The Chaos Computer Club and Digital Courage have consistently campaigned against video surveillance and any form of biometric or behavioural surveillance. The long term effect of these “pilots” is to normalise surveillance.</p></li>
</ul>
@ -1364,19 +1445,19 @@
<p><strong>All the deployments of RBI we are aware of in Germany were conducted by law enforcement</strong>. The deployments range from using facial recognition software to analyse the German central criminal information system, to specific deployments in more targeted locations such as Berlin Südkreuz train station or Mannheim city centre, or to deployments around specific events such as the G20 in Hamburg in 2019.</p>
<section id="pilot-project-südkreuz-berlin" class="level3">
<h3><a class="maplink" data-title="Pilot Project Südkreuz Berlin">Pilot Project Südkreuz Berlin</a></h3>
<p><strong>The German federal police (BPOL)</strong>, in cooperation with the <a class="maplink" data-title="Deutsche Bahn AG">Deutsche Bahn AG</a>, the German railway company, conducted a project called “Sicherheitsbahnhof” at the Berlin railway station Südkreuz in 2017/18. The project consisted of two parts: part one was done from August 2017 until January 2018 with 312 voluntary participants. Part two was carried out from February until July 2018, including 201 participants (Bundespolizeipräsidium Potsdam 2018).</p>
<p><strong><a class="maplink" data-title="German Federal Police (Bundespolizei)">The German federal police</a> (BPOL)</strong>, in cooperation with the <a class="maplink" data-title="Deutsche Bahn AG">Deutsche Bahn AG</a>, the German railway company, conducted a project called “Sicherheitsbahnhof” at the Berlin railway station Südkreuz in 2017/18. The project consisted of two parts: part one was done from August 2017 until January 2018 with 312 voluntary participants. Part two was carried out from February until July 2018, including 201 participants (Bundespolizeipräsidium Potsdam 2018).</p>
<p><strong>For the first project, 77 video cameras and a video management system were installed at the train station Berlin Südkreuz.</strong> Three cameras were used for the <strong>biometric facial recognition during live monitoring</strong>. During the project, the systems BioSurveillance by the company <a class="maplink" data-title="Herta Security">Herta Security</a>, delivered by Dell EMC AG, Morpho Video Investigator (MVI) by <a class="maplink" data-title="IDEMIA">IDEMIA AG</a>, and <a class="maplink" data-title="AnyVision">AnyVision</a> by Anyvision were used and tested. To detect and identify faces, the systems worked based <strong>on neural networks using Template Matching Methods</strong>. For that purpose, images of the faces were recorded and converted into templates. Subsequently, the facial recognition software matched the unknown picture to a known model saved in the reference database. As soon as a certain threshold of similarity is reached, the image is considered a match (see 2.3. for a technical description) The reference database consisted of high-quality images of the participants. That means the photographs had to adhere to quality standards such as a neutral grey background, no shadow in the faces, enough lighting, low compression to avoid artefacts, high resolution, and a straightforward viewing direction (Bundespolizeipräsidium Potsdam 2018).</p>
<p><strong>For the first testing phase,</strong> the participants passed the designated area of the train station Berlin Südkreuz a total of 41.000 times. BioSurveillance had an average hit rate of 68,5%, MVI of 31,7%, and <a class="maplink" data-title="AnyVision">AnyVision</a> 63,1%. <strong>A combined hit rate by the interconnection of the three systems resulted in an increased total hit rate of 84,9%.</strong> <strong>The interconnection also increased the rate of false positives.</strong> The matches were logged but not saved (Bundespolizeipräsidium Potsdam 2018).</p>
<p><strong>For the second testing phase</strong>, the reference database consisted of participant images from the video stream of the first testing phase. For each participant, 2-5 images were extracted from the video stream. The images recorded during the second testing phase generally were of worse quality than from the first phase. All systems used more than one picture as a reference to identify a person (Bundespolizeipräsidium Potsdam 2018). During the second phase, the interconnected systems had <strong>an average testing rate of 91,2%.</strong> BioSurveillance resulted in an average hit rate of 82,8%, MVI in 31,2%, and <a class="maplink" data-title="AnyVision">AnyVision</a> in 76,2%. The performance increased as the systems had more images as a reference (Bundespolizeipräsidium Potsdam 2018).</p>
<p><strong>The <a class="maplink" data-title="Deutsche Bahn AG">Deutsche Bahn AG</a> used the existing infrastructure at the railway station Berlin Südkreuz for an experiment on behavioural analysis starting in June 2019.</strong> The tests were done twice a week during the day. Volunteers performed situations the system should recognise and identify. After scanning people's behaviour, the software would alert the police or the railway company (Henning 2019). <strong>The police assembled a list of behaviours that should be recognised by the system: people lying down or entering certain zones of the train station (such as construction areas), groups of people or streams of people, objects that were set down such as luggage, and the positions of persons and objects</strong>. Furthermore, the system would be counting the number of people in certain areas and allow the analysis of the video data by the police. The software used by the tests is provided by <a class="maplink" data-title="IBM">IBM</a> Germany GmbH, the <a class="maplink" data-title="Hitachi Consortium">Hitachi Consortium</a> (<a class="maplink" data-title="Hitachi">Hitachi</a>, <a class="maplink" data-title="Conef">Conef</a>, MIG), Funkwerk video systems GmbH and G2K Group GmbH (Bundespolizei 2019).</p>
<p><strong>The <a class="maplink" data-title="Deutsche Bahn AG">Deutsche Bahn AG</a> used the existing infrastructure at the railway station Berlin Südkreuz for an experiment on behavioural analysis starting in June 2019.</strong> The tests were done twice a week during the day. Volunteers performed situations the system should recognise and identify. After scanning people's behaviour, the software would alert the police or the railway company (Henning 2019). <strong>The police assembled a list of behaviours that should be recognised by the system: people lying down or entering certain zones of the train station (such as construction areas), groups of people or streams of people, objects that were set down such as luggage, and the positions of persons and objects</strong>. Furthermore, the system would be counting the number of people in certain areas and allow the analysis of the video data by the police. The software used by the tests is provided by <a class="maplink" data-title="IBM Germany Gmbh">IBM Germany GmbH</a>, the <a class="maplink" data-title="Hitachi Consortium">Hitachi Consortium</a> (<a class="maplink" data-title="Hitachi">Hitachi</a>, <a class="maplink" data-title="Conef">Conef</a>, <a class="maplink" data-title="MIG GA">MIG</a>), <a class="maplink" data-title="Funkwerk video systems Gmbh">Funkwerk video systems GmbH</a> and <a class="maplink" data-title="G2K Group Gmbh">G2K Group GmbH</a> (Bundespolizei 2019).</p>
</section>
<section id="hamburg-g20-summit" class="level3">
<h3>Hamburg G20 Summit </h3>
<p><strong>The police in Hamburg used facial recognition software <a class="maplink" data-title="Videmo">Videmo</a> 360 (by <a class="maplink" data-title="Videmo">Videmo</a>)</strong> during the protests against the G20 summit in 2017 (Bröckling 2019). The database, consisting of 100 TB of data, consists of material the police assembled during recording identities in investigations and data from external sources such as surveillance cameras in train stations, the BKA's online portal called “Boston Infrastruktur”, from the internet and the media. <strong>"Boston Infrastruktur" is a web portal accessible to the public in July 2017, where people could upload images and videos</strong>. <strong>All data that concerns the time and place of the G20 summit were included.</strong> Furthermore, data were assembled in 2017 during investigations of the special commission “Schwarzer Block” in the context of the G20 summit protests. The images were first detected and identified, meaning templates of faces were made. <strong>Subsequently, experts checked the material manually (Caspar 2018). The database includes 100.000 individuals in Hamburg during the G20 summit and whose profiles are saved in the police database.</strong> The technology allows for the determination of behaviour, participation in gatherings, preferences, and religious or political engagement (Bröckling 2019).</p>
<p><strong>The police in Hamburg used facial recognition software <a class="maplink" data-title="Videmo">Videmo</a> 360 (by <a class="maplink" data-title="Videmo">Videmo</a>)</strong> during the protests against the <a class="maplink" data-title="Facial Recognition during Hamburg G20 Summit">G20 summit in 2017</a> (Bröckling 2019). The database, consisting of 100 TB of data, consists of material the police assembled during recording identities in investigations and data from external sources such as surveillance cameras in train stations, the BKA's online portal called “Boston Infrastruktur”, from the internet and the media. <strong>"Boston Infrastruktur" is a web portal accessible to the public in July 2017, where people could upload images and videos</strong>. <strong>All data that concerns the time and place of the G20 summit were included.</strong> Furthermore, data were assembled in 2017 during investigations of the special commission “Schwarzer Block” in the context of the G20 summit protests. The images were first detected and identified, meaning templates of faces were made. <strong>Subsequently, experts checked the material manually (Caspar 2018). The database includes 100.000 individuals in Hamburg during the G20 summit and whose profiles are saved in the police database.</strong> The technology allows for the determination of behaviour, participation in gatherings, preferences, and religious or political engagement (Bröckling 2019).</p>
</section>
<section id="mannheim-public-surveillance" class="level3">
<h3><a class="maplink" data-title="Mannheim public surveillance">Mannheim public surveillance</a></h3>
<p><strong>68 cameras were installed by local police on central squares and places in the German city Mannheim to record the moving patterns of people.</strong> In this project, which started in 2018, the software is used to detect conspicuous behaviour. The police are alerted by the cameras and investigate the emerging situation they have observed on camera further (Reuter 2018). The cameras were placed in areas with increased incidences of criminal activity. Only two minutes lie between the alert of the system and the intervention by the police on average. <strong>As the software is learning, it is increasingly able to detect criminal or violent activity. However, sometimes the alerts are not correct, for instance, the system cannot recognise a hug as not dangerous (heise online 2020).</strong> The software is developed by the Fraunhofer Institute of Optronics, System Technologies, and Image Exploitation Karlsruhe and is continuously tested and adapted to be suitable for public spaces. Twenty cameras are used to test the software (Ministerium für Inneres 2020).</p>
<p><strong>68 cameras were installed by local police on central squares and places in the German city Mannheim to record the moving patterns of people.</strong> In this project, which started in 2018, the software developed by the <strong><a class="maplink" data-title="Frauenhofer Institute Karlsruhe">Fraunhofer Institute of Optronics</a> in Karlsruhe</strong> is used to detect conspicuous behaviour. The police are alerted by the cameras and investigate the emerging situation they have observed on camera further (Reuter 2018). The cameras were placed in areas with increased incidences of criminal activity. Only two minutes lie between the alert of the system and the intervention by the police on average. <strong>As the software is learning, it is increasingly able to detect criminal or violent activity. However, sometimes the alerts are not correct, for instance, the system cannot recognise a hug as not dangerous (heise online 2020).</strong> The software is continuously tested and adapted to be suitable for public spaces. Twenty cameras are used to test the software (Ministerium für Inneres 2020).</p>
</section>
</section>
<section id="legal-bases-and-challenges-3" class="level2">
@ -1396,7 +1477,7 @@
<p><strong>The BDSG restricts the application of certain data subject rights</strong> as enshrined in the GDPR such as the right to be informed (§33) and the right to request access (§34). §37 of the Act provides a sectorial exception in relation to providing services pursuant to an insurance contract for the prohibition against the sole automated decision-making. In relation to the processing of personal data for law enforcement purposes, <strong>the BDSG permits the sole automated decision-making if it is authorised by law (§55)</strong>. Nevertheless, the decision cannot be based on sensitive data unless there are suitable safeguards to the data subject (§55(2)). In any case, it <strong>provides an explicit prohibition against conducting profiling that may discriminate against people based on their sensitive data (§55(3)).</strong>  </p>
<p><strong>The collection of personal data in general and facial images in particular in criminal investigation proceedings are authorised under German Law by the Federal Police Act (<em>Gesetz über die Bundespolizei</em>) (BPoIG),</strong> by the Federal Criminal Police Office and the Cooperation of the Federal and State Governments in Criminal Police Matters (<em>Bundeskriminalamtgesetz</em>) (BKAG), the Code of Criminal Procedure (<em>Strafprozessordnung</em>) (StPO), and the police acts of <em>Länder</em>.</p>
<p><strong>The collection of personal data in general and facial images in particular in criminal investigation proceedings are authorised under German Law by the Federal Police Act (<em>Gesetz über die Bundespolizei</em>) (BPoIG),</strong> by the Federal Criminal Police Office and the Cooperation of the Federal and State Governments in Criminal Police Matters (<em><a class="maplink" data-title="German Federal Criminal Police Office (Bundeskriminalamt)">Bundeskriminalamtgesetz</a></em>) (BKAG), the Code of Criminal Procedure (<em>Strafprozessordnung</em>) (StPO), and the police acts of <em>Länder</em>.</p>
<p><strong>§24 of the BPoIG grants the Federal Police the authority to take photographs including image recordings of a person subject to specific conditions</strong>. Moreover, <strong>§26 of the BPoIG</strong>, entrusts the Federal Police the power to collect personal data by making <strong>picture and sound recordings of participants in public events or gatherings</strong> if facts justify that there are significant risks to border security or to categories of people or objects. §27 of the BPoIG further authorises the use of <strong>automatic image recording</strong>, albeit in relation to security risks at the border or to categories of people or objects. Each section provides the obligations for the deletion of the data after a specific timeframe.</p>
@ -1417,7 +1498,7 @@
<p><strong>All of the video data stored by the Hamburg police during the G20 remains stored by the police and even if the Hamburg data protection authority believes that it should be removed, deletion is not currently possible.</strong> This video data includes several days of footage from central Hamburg from 6-10 July 2017 and includes many people going about their daily lives without any indication of committing a crime (Monroy 2018).</p>
<p><strong>Another element of normalisation is in regard to the integration of biometric facial recognition for historical data using the <a class="maplink" data-title="German central criminal information system INPOL">German central criminal information system INPOL</a>.</strong> Historical data of the usage of the systems shows a systematic year on year increase in the number of requests being made to the system by the German police (Monroy 2020), even though the number of criminal offenses has gone down steadily over the past decade (Statista 2021).</p>
<p><img src="images/media/image4.png" style="width:6.07974in;height:3.33433in" alt="Chart, bar chart, histogram Description automatically generated" /></p>
<p>Figure 4. Growth in police requests to INPOL system<a href="#fn37" class="footnote-ref" id="fnref37" role="doc-noteref"><sup>37</sup></a></p>
<p>Figure 4. Growth in police requests to INPOL system<a href="#fn43" class="footnote-ref" id="fnref43" role="doc-noteref"><sup>43</sup></a></p>
</section>
</section>
<section id="the-dragonfly-project-hungary" class="level1 case" data-title="Dragonfly Project">
@ -1442,7 +1523,7 @@
<p>On 10 December 2019 the Hungarian Parliament passed a package of amendments of acts for the work of law enforcement in Hungary. Entitled “the simplification and digitisation of some procedures” this adjustment legalised <strong>the use of forensic – but also live – FRT by the <a class="maplink" data-title="Hungarian Police">Hungarian Police</a></strong> (Hungarian Parliament 2019). In cases when a person identified by the police cannot present an ID document, the police agents can take a photograph of the individual on location, take fingerprints, and record the biometric data based on “perception and measurement” of external characteristics. The photo taken on location can be instantly verified against the database of the national registry of citizens. The <strong>automatic search</strong> is performed by a face recognition algorithm and the five closest matches are returned to the police agent who, based on these photos proceeds with identifying the person (1994. Évi XXXIV. Törvény, para 29/4(a)). This application of FRT does not fall under the category of mass surveillance; however, it is only possible due to <strong>a central system which collects and centralises the national and other biometric databases</strong> but also provides the technical support for accessing it in a quick and affective way by various operational units. In this instance by the patrolling police.  </p>
<section id="the-dragonfly-szitakötő-project" class="level3">
<h3>The Dragonfly (Szitakötő) Project</h3>
<p>In 2018 the Ministry of Interior presented a bill in the Hungarian Government that proposed a <strong>centralised CCTV system with data stored in one centralised database called the Governmental Data Centre</strong> (Kormányzati Adatközpont, abbreviated as KAK). All governmental operations aiming at developing this centralised database run under the name <strong>Szitakötő (Dragonfly)</strong>. This central storage facility collects surveillance data of public spaces (streets, squares, parks, parking facilities, etc.); the Centre for Budapest Transport (BKK); bank security and the Hungarian Public Road PLC. The project with an estimated budget of 50 billion forints (160 million euros) proposes to centralise about <strong>35.000 CCTV cameras and 25.000 terabytes of monitoring data</strong> from across the country (NAIH 2018). While the project, and notably the response of Dr. Attila Péterfalvi, head of the Hungarian Data Protection Authority, - Hungarian National Authority for Data Protection and Freedom of Information (NAIH), who warned of the lack of data protection considerations in the bill, have been largely mediatised, this has done little for halting the Project which has already been rolled out. In 2015 the Hungarian company GVSX Ltd (Hungary). Had already been contracted (NISZ-GVSX 2019) to implement an Integrated Traffic Management and Control System called IKSZR (Integrált Közlekedésszervezési és Szabályozási Rendszer) that centralises data from various systems such as ANPR cameras, car parks, traffic monitoring, meteorological data, etc. The <a class="maplink" data-title="Dragonfly Project">Dragonfly Project</a> has been designed as an expansion of this system by <strong>centralising the data flowing from both the IKSZR system, the databases of the National Infocommunication Services (NISZ) and also CCTV data from other public and private surveillance systems</strong> such as those operated by local governments, public transport companies and banks.</p>
<p>In 2018 the <a class="maplink" data-title="Ministry of Interior (Hungary)">Ministry of Interior</a> presented a bill in the Hungarian Government that proposed a <strong>centralised CCTV system with data stored in one centralised database called the Governmental Data Centre</strong> (Kormányzati Adatközpont, abbreviated as KAK). All governmental operations aiming at developing this centralised database run under the name <strong>Szitakötő (Dragonfly)</strong>. This central storage facility collects surveillance data of public spaces (streets, squares, parks, parking facilities, etc.); the Centre for Budapest Transport (BKK); bank security and the Hungarian Public Road PLC. The project with an estimated budget of 50 billion forints (160 million euros) proposes to centralise about <strong>35.000 CCTV cameras and 25.000 terabytes of monitoring data</strong> from across the country (NAIH 2018). While the project, and notably the response of Dr. Attila Péterfalvi, head of the Hungarian Data Protection Authority, - Hungarian National Authority for Data Protection and Freedom of Information (NAIH), who warned of the lack of data protection considerations in the bill, have been largely mediatised, this has done little for halting the Project which has already been rolled out. In 2015 the Hungarian company GVSX Ltd (Hungary). Had already been contracted (NISZ-GVSX 2019) to implement an Integrated Traffic Management and Control System called IKSZR (Integrált Közlekedésszervezési és Szabályozási Rendszer) that centralises data from various systems such as ANPR cameras, car parks, traffic monitoring, meteorological data, etc. The <a class="maplink" data-title="Dragonfly Project">Dragonfly Project</a> has been designed as an expansion of this system by <strong>centralising the data flowing from both the IKSZR system, the databases of the National Infocommunication Services (NISZ) and also CCTV data from other public and private surveillance systems</strong> such as those operated by local governments, public transport companies and banks.</p>
<p>The technical description of the <a class="maplink" data-title="Dragonfly Project">Dragonfly Project</a> does not make any explicit reference to (live) facial recognition technology, however, the system <strong>collects, stores and searches, in real time, video surveillance footage from 35.000 CCTV cameras</strong>. However, from the reports of the <a class="maplink" data-title="HCLU">Hungarian Civil Liberties Union</a> (HCLU or TASZ in Hungarian) and the DPA, it is known (NAIH 2019, 139) that <strong>to some extend FRT has been used by the Secret Service for National Security (SSNS)</strong>, one of the national security services of Hungary. According to the DPA’s investigation all the cases in which FRT has been used happened <strong>in relation to concrete (criminal) cases looking for a missing person or someone under warrant</strong>. These cases were also <strong>limited to specific geographic locations</strong> (NAIH 2019). According to the DPA’s investigation, in 2019 the <span class="underline"> </span> FRT system operated by the SSNS found 6.000 matches, which resulted in around 250 instances of stop-and-search and 4 arrests (NAIH 2019). The numbers for 2020 are inconsistent with those given for 2019 (3 matches, 28 instances of stop-and-search, unknown number of arrests), however, this is probably due to the fact that <strong>the system has since been moved primarily to the jurisdiction of the <a class="maplink" data-title="Hungarian Police">Hungarian Police</a></strong>.</p>
@ -1466,7 +1547,7 @@
<p>Interestingly, the HKR app, — just like the contact tracing app VirusRadar, which was developed <span class="underline"> </span> by Nextsense — has been <strong>“donated” to the Hungarian Government by <a class="maplink" data-title="Asura Technology">Asura Technologies</a> “free of charge”</strong>. </p>
<p><img src="images/media/image5.png" style="width:3.1203in;height:1.7525in" alt="Graphical user interface, application, chat or text message Description automatically generated" /><img src="images/media/image6.png" style="width:3.10526in;height:1.74405in" alt="A picture containing text, screenshot, monitor Description automatically generated" /></p>
<p>Figure 5. Snapshots from the video Home Quarantine System Short Presentation by Asura Technologies<a href="#fn38" class="footnote-ref" id="fnref38" role="doc-noteref"><sup>38</sup></a></p>
<p>Figure 5. Snapshots from the video Home Quarantine System Short Presentation by Asura Technologies<a href="#fn44" class="footnote-ref" id="fnref44" role="doc-noteref"><sup>44</sup></a></p>
</section>
<section id="legal-bases-and-challenges-4" class="level2">
<h2>Legal bases and challenges</h2>
@ -1511,12 +1592,12 @@
</section>
<section id="recommendations" class="level1">
<h1>Recommendations</h1>
<p><strong>1. The EU should prohibit the deployment of both indiscriminate and “targeted” Remote Biometric and Behavioural Identification technologies in public spaces, as it amounts to mass surveillance.</strong></p>
<p><strong>1. The EU should prohibit the deployment of both indiscriminate and “targeted” Remote Biometric and Behavioural Identification (RBI) technologies in public spaces (real-time RBI), as well as ex-post identification (or forensic RBI). Our analysis shows that both practices, even when used for “targeted surveillance” amount to mass surveillance.</strong></p>
<ul>
<li><p>The EU should <strong>prohibit the deployment of Remote Biometric and Behavioural Identification technologies in public spaces</strong>, in line with similar recommendations made by the EDPB and the EDPS.<a href="#fn39" class="footnote-ref" id="fnref39" role="doc-noteref"><sup>39</sup></a></p></li>
<li><p>This concerns indiscriminate use of such technologies in public spaces and concerns not only the acquisition and processing of <strong>faces, but also gait, voice and other biometric or behavioural signals.</strong></p></li>
<li><p>It also concerns the use of such technologies for <strong>“targeted surveillance”</strong> as the practice might be considered as expansive and intrusive to an extent that it would constitute disproportionate interference with the rights to privacy and personal data protection.</p></li>
</ul>
<li><p>In line with similar recommendations made by the EDPB and the EDPS,<a href="#fn45" class="footnote-ref" id="fnref45" role="doc-noteref"><sup>45</sup></a> the EU should <strong>prohibit the deployment of Remote Biometric and Behavioural Identification technologies in public spaces</strong></p></li>
<li><p>In line with the position of the EDRi regarding’s EU Artificial Intelligence Act<a href="#fn46" class="footnote-ref" id="fnref46" role="doc-noteref"><sup>46</sup></a>, our research supports the notion that the <strong>distinction between "real-time” and “ex-post” is irrelevant</strong> when it comes to the impact of these technologies on fundamental rights. Ex-post identification carries in fact a higher potential of harm, as more data can be pooled from different sources to proceed to the identification. The use of such technologies for <strong>“targeted surveillance” is thus equally harmful</strong> as the practice might be considered as expansive and intrusive to an extent that it would constitute disproportionate interference with the rights to privacy and personal data protection.</p></li>
<li><p>This concerns not only the acquisition and processing of <strong>faces, but also gait, voice and other biometric or behavioural signals.</strong></p></li>
</ul>
<p><strong>2. The EU should strengthen transparency and accountability of biometric and behavioural recognition technologies</strong></p>
<ul>
<li><p>Our research found that the majority of surveillance systems remain opaque. There is very <strong>little information on how citizens' data is processed</strong> when they enter surveilled public spaces. Rarely are <strong>concrete alternatives</strong> provided if they do not wish to be surveilled. In some extreme cases, such as the deployment of FRT trials in London, citizens who deliberately avoided surveillance by covering their faces were <strong>subjected to fines</strong>. This poses considerable challenges to citizens’ rights, as well as to transparency and accountability of these systems.</p></li>
@ -1564,183 +1645,183 @@
</section>
<section id="references" class="level1 list-paragraph">
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<p>Chaos Computer Club. 2018. “Biometrische Videoüberwachung: Der Südkreuz-Versuch war kein Erfolg.” <a target="_blank" href="https://www.ccc.de/de/updates/2018/debakel-am-suedkreuz">https://www.ccc.de/de/updates/2018/debakel-am-suedkreuz</a></p>
<p>Chin, Josh and Clément Bürge. 2017. “Twelve Days in Xinjiang: How China’s Surveillance State Overwhelms Daily Life.” <em>Wall Street Journal</em>, 19 December 2017. https://www.wsj.com/articles/twelve-days-in-xinjiang-how-chinas-surveillance-state-overwhelms-daily-life-1513700355, checked on 4/24/2021.</p>
<p>CNIL. 2018. “Mise en œuvre expérimentale de l’application « REPORTY » par la ville de NICE : quelle est la position de la CNIL ?” https://www.cnil.fr/fr/mise-en-oeuvre-experimentale-de-lapplication-reporty-par-laville-de-nice-quelle-est-la-position-de.</p>
<p>CNIL. 2019a. “Délibération n° 2019-001du 10 janvier 2019 portant règlement type relatif à la mise en œuvre de dispositifs ayant pour finalité le contrôle d'accès par authentification biométrique aux locaux, aux appareils et aux applications informatiques sur les lieux de travail.” <a href="https://www.cnil.fr/sites/default/files/atoms/files/deliberation-2019-001-10-01-2019-reglement-type-controle-dacces-biometrique.pdf">https://www.cnil.fr/sites/default/files/atoms/files/deliberation-2019-001-10-01-2019-reglement-type-controle-dacces-biometrique.pdf</a></p>
<p>CNIL. 2019a. “Délibération n° 2019-001du 10 janvier 2019 portant règlement type relatif à la mise en œuvre de dispositifs ayant pour finalité le contrôle d'accès par authentification biométrique aux locaux, aux appareils et aux applications informatiques sur les lieux de travail.” <a target="_blank" href="https://www.cnil.fr/sites/default/files/atoms/files/deliberation-2019-001-10-01-2019-reglement-type-controle-dacces-biometrique.pdf">https://www.cnil.fr/sites/default/files/atoms/files/deliberation-2019-001-10-01-2019-reglement-type-controle-dacces-biometrique.pdf</a></p>
<p>CNIL. 2019b. “<em>Reconnaissance faciale - pour un débat à la hauteur des enjeux.</em>” <em>Paris: Commission National Informatique et Libertés</em>.</p>
<p>Cochior, Cristina. and Ruben van de Ven. 2020. “Plotting Data: Acts of Collection and Omission.” <a href="http://plottingd.at/a/introduction.html">http://plottingd.at/a/introduction.html</a></p>
<p>Colomé, Jordi Pérez. 2019. “Marbella, the biggest video surveillance lab in Spain.” <em>EL PAÍS</em>. 29 November 2019. <a href="https://english.elpais.com/elpais/2019/11/27/inenglish/1574849134_892168.html">https://english.elpais.com/elpais/2019/11/27/inenglish/1574849134_892168.html</a>.</p>
<p>Cochior, Cristina. and Ruben van de Ven. 2020. “Plotting Data: Acts of Collection and Omission.” <a target="_blank" href="http://plottingd.at/a/introduction.html">http://plottingd.at/a/introduction.html</a></p>
<p>Colomé, Jordi Pérez. 2019. “Marbella, the biggest video surveillance lab in Spain.” <em>EL PAÍS</em>. 29 November 2019. <a target="_blank" href="https://english.elpais.com/elpais/2019/11/27/inenglish/1574849134_892168.html">https://english.elpais.com/elpais/2019/11/27/inenglish/1574849134_892168.html</a>.</p>
<p>Council of Europe. 2018. “The Practical Guide on the Use of Personal Data in the Police Sector.” https://rm.coe.int/t-pd-201-01-practical-guide-on-the-use-of-personal-data-in-the-police-/16807927d5</p>
<p>Council of Europe. 2020. “Digital Solutions to Fight Covid-19. 2020 Data Protection Report.” <a href="https://rm.coe.int/prems-120820-gbr-2051-digital-solutions-to-fight-covid-19-text-a4-web-/16809fe49c">https://rm.coe.int/prems-120820-gbr-2051-digital-solutions-to-fight-covid-19-text-a4-web-/16809fe49c</a></p>
<p>Council of Europe. 2021. “Guidelines on Facial Recognition.” <a href="https://rm.coe.int/guidelines-on-facial-recognition/1680a134f3">https://rm.coe.int/guidelines-on-facial-recognition/1680a134f3</a></p>
<p>Csaky, Zselyke. 2020. “Nations in Tranzit 2020. Dropping the Democratic Facade.” <em>Freedom House.</em> <a href="https://freedomhouse.org/sites/default/files/2020-04/05062020_FH_NIT2020_vfinal.pdf">https://freedomhouse.org/sites/default/files/2020-04/05062020_FH_NIT2020_vfinal.pdf</a> .</p>
<p>De Halleux, Françoise. 2020. ‘Reconnaissance faciale: le ministre de l’Intérieur, Pieter De Crem, n’y renonce pas! (29/06/2020)’. <em>Édition digitale de Liège</em>. <a href="https://lameuse.sudinfo.be/591608/article/2020-06-29/reconnaissance-faciale-le-ministre-de-linterieur-pieter-de-crem-ny-renonce-pas">https://lameuse.sudinfo.be/591608/article/2020-06-29/reconnaissance-faciale-le-ministre-de-linterieur-pieter-de-crem-ny-renonce-pas</a> (September 29, 2021).</p>
<p>Council of Europe. 2020. “Digital Solutions to Fight Covid-19. 2020 Data Protection Report.” <a target="_blank" href="https://rm.coe.int/prems-120820-gbr-2051-digital-solutions-to-fight-covid-19-text-a4-web-/16809fe49c">https://rm.coe.int/prems-120820-gbr-2051-digital-solutions-to-fight-covid-19-text-a4-web-/16809fe49c</a></p>
<p>Council of Europe. 2021. “Guidelines on Facial Recognition.” <a target="_blank" href="https://rm.coe.int/guidelines-on-facial-recognition/1680a134f3">https://rm.coe.int/guidelines-on-facial-recognition/1680a134f3</a></p>
<p>Csaky, Zselyke. 2020. “Nations in Tranzit 2020. Dropping the Democratic Facade.” <em>Freedom House.</em> <a target="_blank" href="https://freedomhouse.org/sites/default/files/2020-04/05062020_FH_NIT2020_vfinal.pdf">https://freedomhouse.org/sites/default/files/2020-04/05062020_FH_NIT2020_vfinal.pdf</a> .</p>
<p>De Halleux, Françoise. 2020. ‘Reconnaissance faciale: le ministre de l’Intérieur, Pieter De Crem, n’y renonce pas! (29/06/2020)’. <em>Édition digitale de Liège</em>. <a target="_blank" href="https://lameuse.sudinfo.be/591608/article/2020-06-29/reconnaissance-faciale-le-ministre-de-linterieur-pieter-de-crem-ny-renonce-pas">https://lameuse.sudinfo.be/591608/article/2020-06-29/reconnaissance-faciale-le-ministre-de-linterieur-pieter-de-crem-ny-renonce-pas</a> (September 29, 2021).</p>
<p>De Hert, Paul. 2017. “Courts, privacy and data protection in Belgium: Fundamental rights that might as well be struck from the Constitution” in Brkan, Maja and Psychogiopoulou, <em>Evangelia. Courts, Privacy and Data Protection in the Digital Environment</em>, Maastricht: Edward Elgar, 63-81.</p>
<p>Défenseur des Droits. 2021. <em>Technologies Biométriques : L’impératif Respect Des Droits Fondamentaux</em>. Paris: Défenseur des Droits. <a href="https://www.defenseurdesdroits.fr/sites/default/files/atoms/files/rap-biometr-num-08.07.21.pdf">https://www.defenseurdesdroits.fr/sites/default/files/atoms/files/rap-biometr-num-08.07.21.pdf</a> (September 29, 2021).</p>
<p>Défenseur des Droits. 2021. <em>Technologies Biométriques : L’impératif Respect Des Droits Fondamentaux</em>. Paris: Défenseur des Droits. <a target="_blank" href="https://www.defenseurdesdroits.fr/sites/default/files/atoms/files/rap-biometr-num-08.07.21.pdf">https://www.defenseurdesdroits.fr/sites/default/files/atoms/files/rap-biometr-num-08.07.21.pdf</a> (September 29, 2021).</p>
<p>Delver, Guido. 2021. Phone interview on 29-03-2021. <em>Interviewer: Ruben van de Ven</em></p>
<p>Dercsényi, Dávid. 2018. "Totális megfigyelés 50 milliárdért - Pintérék terve kiakasztotta az adatvédelmi biztost". <em>hvg.hu</em>. 2 December 2018. <a href="https://hvg.hu/itthon/20181202_NAIH_50_milliardos_totalis_megfigyeles_lenne_Pinterek_kamerarendszere">https://hvg.hu/itthon/20181202_NAIH_50_milliardos_totalis_megfigyeles_lenne_Pinterek_kamerarendszere</a>.</p>
<p>DPA Hamburg. 2018. “Datenschutzrechtliche Prüfung des Einsatzes einer Gesichtserkennungssoftware zur Aufklärung von Straftaten im Zusammenhang mit dem G20-Gipfel durch die Polizei Hamburg.” Hamburg: DPA Hamburg. <a href="https://datenschutz-hamburg.de/assets/pdf/Pruefbericht_Gesichtserkennungssoftware.pdf">https://datenschutz-hamburg.de/assets/pdf/Pruefbericht_Gesichtserkennungssoftware.pdf</a></p>
<p>Dudebout, Camille. 2020. “Safe City Project in Nice: Testing Facial Recognition.” <a href="https://ai-regulation.eu/safe-city-projectin-nice-testing-facial-recognition/">https://ai-regulation.eu/safe-city-projectin-nice-testing-facial-recognition/</a>.</p>
<p>EDPB 2019. Guidelines 3/2019 on processing of personal data through video devices, <em>edpb.europa.eu.</em> <a href="https://edpb.europa.eu/sites/edpb/files/consultation/edpb_guidelines_201903_videosurveillance.pdf">https://edpb.europa.eu/sites/edpb/files/consultation/edpb_guidelines_201903_videosurveillance.pdf</a>.</p>
<p>EDPB. 2021a. “EDPB &amp; EDPS Call for Ban on Use of AI for Automated Recognition of Human Features in Publicly Accessible Spaces, and Some Other Uses of AI That Can Lead to Unfair Discrimination | European Data Protection Board.” <a href="https://edpb.europa.eu/news/news/2021/edpb-edps-call-ban-use-ai-automated-recognition-human-features-publicly-accessible_en">https://edpb.europa.eu/news/news/2021/edpb-edps-call-ban-use-ai-automated-recognition-human-features-publicly-accessible_en</a>.</p>
<p>Dercsényi, Dávid. 2018. "Totális megfigyelés 50 milliárdért - Pintérék terve kiakasztotta az adatvédelmi biztost". <em>hvg.hu</em>. 2 December 2018. <a target="_blank" href="https://hvg.hu/itthon/20181202_NAIH_50_milliardos_totalis_megfigyeles_lenne_Pinterek_kamerarendszere">https://hvg.hu/itthon/20181202_NAIH_50_milliardos_totalis_megfigyeles_lenne_Pinterek_kamerarendszere</a>.</p>
<p>DPA Hamburg. 2018. “Datenschutzrechtliche Prüfung des Einsatzes einer Gesichtserkennungssoftware zur Aufklärung von Straftaten im Zusammenhang mit dem G20-Gipfel durch die Polizei Hamburg.” Hamburg: DPA Hamburg. <a target="_blank" href="https://datenschutz-hamburg.de/assets/pdf/Pruefbericht_Gesichtserkennungssoftware.pdf">https://datenschutz-hamburg.de/assets/pdf/Pruefbericht_Gesichtserkennungssoftware.pdf</a></p>
<p>Dudebout, Camille. 2020. “Safe City Project in Nice: Testing Facial Recognition.” <a target="_blank" href="https://ai-regulation.eu/safe-city-projectin-nice-testing-facial-recognition/">https://ai-regulation.eu/safe-city-projectin-nice-testing-facial-recognition/</a>.</p>
<p>EDPB 2019. Guidelines 3/2019 on processing of personal data through video devices, <em>edpb.europa.eu.</em> <a target="_blank" href="https://edpb.europa.eu/sites/edpb/files/consultation/edpb_guidelines_201903_videosurveillance.pdf">https://edpb.europa.eu/sites/edpb/files/consultation/edpb_guidelines_201903_videosurveillance.pdf</a>.</p>
<p>EDPB. 2021a. “EDPB &amp; EDPS Call for Ban on Use of AI for Automated Recognition of Human Features in Publicly Accessible Spaces, and Some Other Uses of AI That Can Lead to Unfair Discrimination | European Data Protection Board.” <a target="_blank" href="https://edpb.europa.eu/news/news/2021/edpb-edps-call-ban-use-ai-automated-recognition-human-features-publicly-accessible_en">https://edpb.europa.eu/news/news/2021/edpb-edps-call-ban-use-ai-automated-recognition-human-features-publicly-accessible_en</a>.</p>
<p>EDPB. 2021b. “Thirtieth Plenary Session: EDPB response to NGOs on Hungarian Decrees and statement on Article 23 GDPR.” https://edpb.europa.eu/news/news/2020/thirtieth-plenary-session-edpb-response-ngos-hungarian-decrees-and-statement-article_en.</p>
<p>EDPS and EDPB. 2021. “Joint Opinion on the Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)” <a href="https://edps.europa.eu/data-protection/our-work/publications/opinions/joint-opinion-edps-edps-proposal-regulation-european_en">https://edps.europa.eu/data-protection/our-work/publications/opinions/joint-opinion-edps-edps-proposal-regulation-european_en</a></p>
<p>EDPS. 2020. Shaping a Safer Digital Future: a New Strategy for a New Decade. Released 30 June 2020. <a href="https://edps.europa.eu/press-publications/publications/strategy/shaping-safer-digital-future_en">https://edps.europa.eu/press-publications/publications/strategy/shaping-safer-digital-future_en</a>. Accessed on 16 March 2021.</p>
<p>EDPS. 2021. “Artificial Intelligence Act: a welcomed initiative, but ban on remote biometric identification in public space is necessary.” <a href="https://edps.europa.eu/press-publications/press-news/press-releases/2021/artificial-intelligence-act-welcomed-initiative_en">https://edps.europa.eu/press-publications/press-news/press-releases/2021/artificial-intelligence-act-welcomed-initiative_en</a></p>
<p>EDPS and EDPB. 2021. “Joint Opinion on the Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)” <a target="_blank" href="https://edps.europa.eu/data-protection/our-work/publications/opinions/joint-opinion-edps-edps-proposal-regulation-european_en">https://edps.europa.eu/data-protection/our-work/publications/opinions/joint-opinion-edps-edps-proposal-regulation-european_en</a></p>
<p>EDPS. 2020. Shaping a Safer Digital Future: a New Strategy for a New Decade. Released 30 June 2020. <a target="_blank" href="https://edps.europa.eu/press-publications/publications/strategy/shaping-safer-digital-future_en">https://edps.europa.eu/press-publications/publications/strategy/shaping-safer-digital-future_en</a>. Accessed on 16 March 2021.</p>
<p>EDPS. 2021. “Artificial Intelligence Act: a welcomed initiative, but ban on remote biometric identification in public space is necessary.” <a target="_blank" href="https://edps.europa.eu/press-publications/press-news/press-releases/2021/artificial-intelligence-act-welcomed-initiative_en">https://edps.europa.eu/press-publications/press-news/press-releases/2021/artificial-intelligence-act-welcomed-initiative_en</a></p>
<p>EDRi. 2020. “Ban Biometric Mass Surveillance!” https://edri.org/our-work/blog-ban-biometric-mass-surveillance/</p>
<p>EDRi. 2021. “EU’s AI law needs major changes to prevent discrimination and mass surveillance.” <a href="https://edri.org/our-work/eus-ai-law-needs-major-changes-to-prevent-discrimination-and-mass-surveillance/">https://edri.org/our-work/eus-ai-law-needs-major-changes-to-prevent-discrimination-and-mass-surveillance/</a></p>
<p>EDRi. 2021. “EU’s AI law needs major changes to prevent discrimination and mass surveillance.” <a target="_blank" href="https://edri.org/our-work/eus-ai-law-needs-major-changes-to-prevent-discrimination-and-mass-surveillance/">https://edri.org/our-work/eus-ai-law-needs-major-changes-to-prevent-discrimination-and-mass-surveillance/</a></p>
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<p>European Commission. 2020b. <em>White Paper on Artificial Intelligence: A European Approach to Excellence and Trust</em>. Brussels: European Commission. <a target="_blank" href="https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en">https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en</a></p>
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<p>European Commission. 2021b. <em>Proposal for a Regulation of the European Parliament and the Council Laying down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) COM(2021) 206 Final</em>. Brussels: European Commission. <a href="https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206">https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206</a></p>
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<p>European Parliament. 2021. “European Parliament resolution of 20 January 2021 on artificial intelligence: questions of interpretation and application of international law in so far as the EU is affected in the areas of civil and military uses and of state authority outside the scope of criminal justice.” <a href="https://www.europarl.europa.eu/doceo/document/TA-9-2021-0009_EN.html">https://www.europarl.europa.eu/doceo/document/TA-9-2021-0009_EN.html</a>. Accessed 16 March 2021.</p>
<p>European People’s Party. 2021. “Facial recognition software: regulation instead of ban.” <a href="https://www.eppgroup.eu/newsroom/news/facial-recognition-software-regulation-instead-of-ban">https://www.eppgroup.eu/newsroom/news/facial-recognition-software-regulation-instead-of-ban</a>.</p>
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</section>
<section id="annex-cases" class="level1 list-paragraph">
<h1 class="list-paragraph nocount">ANNEX: CASES</h1>
@ -1780,45 +1861,55 @@
<section class="footnotes" role="doc-endnotes">
<hr />
<ol>
<li id="fn1" role="doc-endnote"><p>https://edps.europa.eu/press-publications/press-news/press-releases/2021/edpb-edps-call-ban-use-ai-automated-recognition_en<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn2" role="doc-endnote"><p>The one-and-a half meter monitor is trained on the COCO dataset, published by <strong>Microsoft</strong> and <strong><a class="maplink" data-title="Facebook AI Research">Facebook AI</a></strong><a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn3" role="doc-endnote"><p>Relatedly, see the Spotify controversy (Access Now 2021)<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn4" role="doc-endnote"><p>For example, a in a 4K UHD image, composed of 3840 × 2160 pixels, a face occupying 300 x 300 pixels would need to occupy approximately 1/100<sup>th</sup> of the screen’s surface. In a HD image composed of 1920 x 1080 pixels, the same 300 x 300 pixel face would occupy about 1/25<sup>th</sup> of the screen’s surface.<a href="#fnref4" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn5" role="doc-endnote"><p>“The Viola–Jones object detection framework is an object detection framework which was proposed in 2001 by Paul Viola and Michael Jones. Although it can be trained to detect a variety of object classes, it was motivated primarily by the problem of face detection.” Wikipedia, “Viola–Jones object detection framework” <a href="https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework">https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework</a><a href="#fnref5" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn6" role="doc-endnote"><p>See: <a href="http://vis-www.cs.umass.edu/lfw/results.html">http://vis-www.cs.umass.edu/lfw/results.html</a>, <a href="https://cocodataset.org/#detection-leaderboard">https://cocodataset.org/#detection-leaderboard</a> and <a href="https://www.nist.gov/programs-projects/face-challenges">https://www.nist.gov/programs-projects/face-challenges</a>.<a href="#fnref6" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn7" role="doc-endnote"><p>Both projects were shut down by the <strong>CNIL,</strong> the French DPA.<a href="#fnref7" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn8" role="doc-endnote"><p>The project was shut down by the <strong><a class="maplink" data-title="Swedish Authority for Privacy Protection (IMY)">Swedish Authority for Privacy Protection (IMY)</a></strong><a href="#fnref8" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn9" role="doc-endnote"><p>Criminal identification database, used by the <strong><a class="maplink" data-title="Austrian Criminal Intelligence Service">Austrian Criminal Intelligence Service</a>.</strong><a href="#fnref9" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn10" role="doc-endnote"><p>The KASTU system interrogates two datasets: the <strong>Registered persons identifying features database (RETU)</strong> and <strong>Aliens database.</strong> It is managed by the <a class="maplink" data-title="National Bureau of Investigation (NBI)">National Bureau of Investigation (NBI)</a>, and can be used by the <strong><a class="maplink" data-title="Finnish Police">Finnish Police</a></strong>, the <strong><a class="maplink" data-title="Finnish Border Guard">Finnish Border Guard</a></strong> and the <strong><a class="maplink" data-title="Finnish Customs">Finnish Customs</a></strong>.<a href="#fnref10" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn11" role="doc-endnote"><p>Criminal case history database, managed by the <strong>French Ministry of Interior</strong><a href="#fnref11" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn12" role="doc-endnote"><p>Criminal case management system, managed by the <strong>German Federal Criminal Police Office</strong> (Bundeskriminalamt)<a href="#fnref12" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn13" role="doc-endnote"><p>Managed by the <strong>Video and Image Laboratory</strong> of the Audiovisual Evidence of the Department of Photography and Modus Operandi of the <a class="maplink" data-title="Hellenic Police Forensic Science Division">Hellenic Police Forensic Science Division</a><a href="#fnref13" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn14" role="doc-endnote"><p>The Facial Image registry is interrogated through a search engine developed by <a class="maplink" data-title="NEC">NEC</a>, and accessible to <strong>the</strong> <strong>National Investigation Agency</strong>, <strong>the</strong> <strong><a class="maplink" data-title="Criminal Courts (Hungary)">Criminal Courts</a></strong>, <strong>the</strong> <strong>National Protective Service</strong>, <strong>the</strong> <strong><a class="maplink" data-title="Counter-Terrorism Centre (Hungary)">Counter-Terrorism Centre</a></strong>, <strong>the</strong> <strong>Hungarian Prison Service</strong>, <strong>the Prosecution Service of Hungary</strong>, t<strong>he Public Administration</strong>, <strong>the</strong> <strong>Special Service for National Security</strong>, <strong>the</strong> <strong>Intelligence Agencies</strong>, <strong>the</strong> <strong><a class="maplink" data-title="Hungarian Police">Hungarian Police</a></strong>, t<strong>he Hungarian Parliamentary Guard</strong>, <strong><a class="maplink" data-title="Hungarian Ministry of Justice">Hungarian Ministry of Justice</a></strong>, <strong>Witness Protection Service</strong>, <strong>the</strong> <strong>National Directorate-General for Aliens Policing and Institution of the President of the Republic.</strong><a href="#fnref14" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn15" role="doc-endnote"><p>Automated Fingerprint Identification System. The system can be interrogated via a software developed by the company <strong><a class="maplink" data-title="Reco 3.26">Reco 3.26</a></strong>, a subsidiary of <strong><a class="maplink" data-title="Parsec 3.26">Parsec 3.26</a></strong>. Another software used is provided by the japanese company <strong><a class="maplink" data-title="NEC">NEC</a></strong>.<a href="#fnref15" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn16" role="doc-endnote"><p>Biometric Data Processing System (criminal data array), supported by database software from <strong><a class="maplink" data-title="RIX Technologies">RIX Technologies</a></strong><a href="#fnref16" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn17" role="doc-endnote"><p>Habitoscopic Data Register<a href="#fnref17" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn18" role="doc-endnote"><p>Central Automatic TeChnology for Recognition of Persons, managed by the <strong><a class="maplink" data-title="Centrum voor Biometrie">Centrum voor Biometrie</a></strong>, connected to the <strong><a class="maplink" data-title="Dutch Judicial Information Service (Justid)">Dutch Judicial Information Service (Justid)</a>.</strong><a href="#fnref18" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn19" role="doc-endnote"><p>The database uses <strong>VeriLook</strong> and <strong>Face Trace</strong> software from the Lithuanian company <strong><a class="maplink" data-title="Neurotechnology">Neurotechnology</a>.</strong><a href="#fnref19" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn20" role="doc-endnote"><p>Automated Biometric Identification System, searchable by the <strong>IntellQ</strong> software from the company <strong><a class="maplink" data-title="IntellByte">IntellByte</a>,</strong> managed by the <strong><a class="maplink" data-title="Ministry of Interior (Croatia)">Ministry of the Interior</a> (Croatia).</strong><a href="#fnref20" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn21" role="doc-endnote"><p>Central Biometric Information System<a href="#fnref21" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn22" role="doc-endnote"><p>National Biometric Identification System<a href="#fnref22" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn23" role="doc-endnote"><p>Managed by the <strong>Photographic and Graphic Laboratory of Criminalistic Services,</strong> using search software by the company <strong><a class="maplink" data-title="Unidas">Unidas</a></strong><a href="#fnref23" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn24" role="doc-endnote"><p><a class="maplink" data-title="IFRS (Interpol)">Interpol Facial Recognition System</a><a href="#fnref24" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn25" role="doc-endnote"><p>Source: TELEFI Report p.23. [[ NOTE TO THE GREENS: A new map should be made to match report’s design at a later stage in the publication process ]]<a href="#fnref25" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn26" role="doc-endnote"><p>As detailed in CHAPTER 4. However, that does not mean that it is not subjected to similar legal frameworks.<a href="#fnref26" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn27" role="doc-endnote"><p>Developed as a partnership between the <strong>Dutch Ministry of Justice &amp; Security</strong>, the <strong><a class="maplink" data-title="Dutch Institute for Technology Safety and Security (DITSS)">Dutch Institute for Technology Safety and Security (DITSS)</a></strong>, the <strong><a class="maplink" data-title="Rotterdam Municipality">Rotterdam Municipality</a></strong>, insurance company <strong><a class="maplink" data-title="Interpolis">Interpolis</a></strong>, the <strong><a class="maplink" data-title="Dutch Police">Dutch Police</a></strong>, the <strong><a class="maplink" data-title="ViNotion">ViNotion</a></strong>, the <strong><a class="maplink" data-title="Avans Hogeschool">Avans Hogeschool</a></strong>, the <strong><a class="maplink" data-title="Munisense">Munisense</a></strong>, the <strong><a class="maplink" data-title="Sustainder">Sustainder</a></strong>, the <strong><a class="maplink" data-title="Twente University">Twente University</a></strong>, the <strong>Max Planck Institute</strong> <strong>for the Study of Crime</strong>, the <strong>Security and Law</strong> and <strong><a class="maplink" data-title="The Network Institute">The Network Institute</a> (Vrije Universiteit Amsterdam).</strong><a href="#fnref27" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn28" role="doc-endnote"><p>Developed in partnership between the <strong><a class="maplink" data-title="Dutch Institute for Technology Safety and Security (DITSS)">Dutch Institute for Technology Safety and Security (DITSS)</a></strong>, <strong><a class="maplink" data-title="Atos">Atos</a></strong>, <strong>the <a class="maplink" data-title="Municipality of Eindhoven">Municipality of Eindhoven</a></strong>, <strong><a class="maplink" data-title="Tilburg University">Tilburg University</a></strong>, <strong><a class="maplink" data-title="Eindhoven University of Technology">Eindhoven University of Technology</a></strong>, <strong><a class="maplink" data-title="Intel">Intel</a></strong>, <strong><a class="maplink" data-title="Sorama">Sorama</a></strong>, and <strong>Axis Communications</strong>; it uses search software from <strong><a class="maplink" data-title="Oddity.ai">Oddity.ai</a></strong> and <strong><a class="maplink" data-title="ViNotion">ViNotion</a></strong>.<a href="#fnref28" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn29" role="doc-endnote"><p>The Defenseur des Droits is a governmental watchdog on civil rights and liberties in France. See Defenseur des Droits (2021) for the call for a ban on facial recognition.<a href="#fnref29" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn30" role="doc-endnote"><p>While the <strong>eID project</strong> is not specific to Belgium, the country stands out for having piloted the project ahead of other EU member states. eID is a form of authentication rather than surveillance system - yet the constitution of a database of machine-readable identities participates to the construction of a digital infrastructure of surveillance that can be misuedmisused for biometric mass surveillance., as argued in chapter 3<a href="#fnref30" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn31" role="doc-endnote"><p>The COC, or Supervisory Body for Police Information is « the autonomous federal parliamentary body in charge of monitoring the management of police information and also the data controller for the integrated police service, the Passenger Information Unit and the General Inspectorate of the Federal and the Local Police. » (Organe de Controle de l'Information Policière 2021).<a href="#fnref31" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn32" role="doc-endnote"><p>https://reclaimyourface.eu/<a href="#fnref32" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn33" role="doc-endnote"><p>The other companies are: Arclan Systems, Business Card Associates, <a class="maplink" data-title="Deveryware">Deveryware</a>, Egidium, Gemalto, Geol Semantics, Igo, Inria, Luceor, Onhys, <a class="maplink" data-title="IDEMIA">IDEMIA</a>, Sys, Sysnav and Yncrea.<a href="#fnref33" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn34" role="doc-endnote"><p>Banque Publique d’Investissement: French Public Investment Bank<a href="#fnref34" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn35" role="doc-endnote"><p>Comité de la Filière industrielle de la sécurité<a href="#fnref35" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn36" role="doc-endnote"><p>For the campaign, see: http://www.technopolice.fr<a href="#fnref36" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn37" role="doc-endnote"><p>Image from https://netzpolitik.org/2020/deutlich-mehr-gesichtserkennung-bei-bundespolizei-und-kriminalaemtern/<a href="#fnref37" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn38" role="doc-endnote"><p>https://youtu.be/5WD9b6tWC0Q<a href="#fnref38" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn39" role="doc-endnote"><p>https://edps.europa.eu/press-publications/press-news/press-releases/2021/edpb-edps-call-ban-use-ai-automated-recognition_en<a href="#fnref39" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn1" role="doc-endnote"><p><a href="https://edps.europa.eu/press-publications/press-news/press-releases/2021/edpb-edps-call-ban-use-ai-automated-recognition_en" target="_blank">https://edps.europa.eu/press-publications/press-news/press-releases/2021/edpb-edps-call-ban-use-ai-automated-recognition_en</a><a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn2" role="doc-endnote"><p><a href="https://edri.org/wp-content/uploads/2021/08/European-Digital-Rights-EDRi-submission-to-European-Commission-adoption-consultation-on-the-Artificial-Intelligence-Act-August-2021.pdf" target="_blank">https://edri.org/wp-content/uploads/2021/08/European-Digital-Rights-EDRi-submission-to-European-Commission-adoption-consultation-on-the-Artificial-Intelligence-Act-August-2021.pdf</a><a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn3" role="doc-endnote"><p>The one-and-a half meter monitor is trained on the COCO dataset, published by <strong>Microsoft</strong> and <strong><a class="maplink" data-title="Facebook AI Research">Facebook AI</a></strong><a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn4" role="doc-endnote"><p>Relatedly, see the Spotify controversy (Access Now 2021)<a href="#fnref4" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn5" role="doc-endnote"><p>Partners in the <a class="maplink" data-title="Living Lab International Zone">Living Lab International Zone</a> include: <a class="maplink" data-title="Municipality of The Hague">Municipality of The Hague</a>, <a class="maplink" data-title="Municipality of The Hague">The Hague Police Region</a>, <a class="maplink" data-title="TNO">TNO</a>, <a class="maplink" data-title="Thales">Thales</a>, <a class="maplink" data-title="Thales">Sorama</a>, <a class="maplink" data-title="Connection Systems">Connection Systems</a>, Crowd Sense, The Hague Security Region, <a class="maplink" data-title="Europol">Europol</a>, <a class="maplink" data-title="Eurojust">Eurojust</a>, <a class="maplink" data-title="OPCW">OPCW</a>, <a class="maplink" data-title="IRMCT">IRMCT</a>, <a class="maplink" data-title="Peace Palace">Peace Palace</a>, <a class="maplink" data-title="Catshuis">Catshuis</a>, Government Buildings Agency, <a class="maplink" data-title=" Dutch Ministry of Foreign Affairs">Ministry of Foreign Affairs</a> and <a class="maplink" data-title="The Hague Security Delta (HSD)">The Hague Security Delta</a>.<a href="#fnref5" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn6" role="doc-endnote"><p>For example, a in a 4K UHD image, composed of 3840 × 2160 pixels, a face occupying 300 x 300 pixels would need to occupy approximately 1/100<sup>th</sup> of the screen’s surface. In a HD image composed of 1920 x 1080 pixels, the same 300 x 300 pixel face would occupy about 1/25<sup>th</sup> of the screen’s surface.<a href="#fnref6" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn7" role="doc-endnote"><p>“The Viola–Jones object detection framework is an object detection framework which was proposed in 2001 by Paul Viola and Michael Jones. Although it can be trained to detect a variety of object classes, it was motivated primarily by the problem of face detection.” Wikipedia, “Viola–Jones object detection framework” <a target="_blank" href="https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework">https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework</a><a href="#fnref7" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn8" role="doc-endnote"><p>See: <a target="_blank" href="http://vis-www.cs.umass.edu/lfw/results.html">http://vis-www.cs.umass.edu/lfw/results.html</a>, <a target="_blank" href="https://cocodataset.org/#detection-leaderboard">https://cocodataset.org/#detection-leaderboard</a> and <a target="_blank" href="https://www.nist.gov/programs-projects/face-challenges">https://www.nist.gov/programs-projects/face-challenges</a>.<a href="#fnref8" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn9" role="doc-endnote"><p>Both projects were shut down by the <strong>CNIL,</strong> the French DPA.<a href="#fnref9" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn10" role="doc-endnote"><p>The project was shut down by the <strong><a class="maplink" data-title="Swedish Authority for Privacy Protection (IMY)">Swedish Authority for Privacy Protection (IMY)</a></strong><a href="#fnref10" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn11" role="doc-endnote"><p>Criminal identification database, used by the <strong><a class="maplink" data-title="Austrian Criminal Intelligence Service">Austrian Criminal Intelligence Service</a></strong>, managed by the <strong><a class="maplink" data-title="Ministry of the Interior (Austria)">Austrian Ministry of Interior</a>.</strong><a href="#fnref11" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn12" role="doc-endnote"><p>The KASTU system interrogates two datasets: the <strong>Registered persons identifying features database (RETU)</strong> and <strong>Aliens database.</strong> It is managed by the <a class="maplink" data-title="National Bureau of Investigation (NBI)">National Bureau of Investigation (NBI)</a>, and can be used by the <strong><a class="maplink" data-title="Finnish Police">Finnish Police</a></strong>, the <strong><a class="maplink" data-title="Finnish Border Guard">Finnish Border Guard</a></strong> and the <strong><a class="maplink" data-title="Finnish Customs">Finnish Customs</a></strong>.<a href="#fnref12" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn13" role="doc-endnote"><p>Criminal case history database, managed by the <strong><a class="maplink" data-title="Ministry of Interior (France)">French Ministry of Interior</a></strong><a href="#fnref13" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn14" role="doc-endnote"><p>Criminal case management system, managed by the <strong><a class="maplink" data-title="German Federal Criminal Police Office (Bundeskriminalamt)">German Federal Criminal Police Office</a></strong> (Bundeskriminalamt)<a href="#fnref14" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn15" role="doc-endnote"><p>Managed by the <strong><a class="maplink" data-title="Video and Image Laboratory (Greek Police)">Video and Image Laboratory</a></strong> of the Audiovisual Evidence of the Department of Photography and Modus Operandi of the <a class="maplink" data-title="Hellenic Police Forensic Science Division">Hellenic Police Forensic Science Division</a><a href="#fnref15" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn16" role="doc-endnote"><p>The Facial Image registry is interrogated through a search engine developed by <a class="maplink" data-title="NEC">NEC</a>, and accessible to <strong>the</strong> <strong><a class="maplink" data-title="National Investigation Agency (Hungary)">National Investigation Agency</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="Criminal Courts (Hungary)">Criminal Courts</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="National Protective Service (Hungary)">National Protective Service</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="Counter-Terrorism Centre (Hungary)">Counter-Terrorism Centre</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="Hungarian Prison Service (Hungary)">Hungarian Prison Service</a></strong>, <strong>the <a class="maplink" data-title="The Prosecution Service of Hungary">Prosecution Service of Hungary</a></strong>, <strong>the <a class="maplink" data-title="The Public Administration (Hungary)">Public Administration</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="Special Service for National Security (Hungary)">Special Service for National Security</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="Intelligence Agencies (Hungary)">Intelligence Agencies</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="Hungarian Police">Hungarian Police</a></strong>, <strong>the <a class="maplink" data-title="The Hungarian Parliamentary Guard">Hungarian Parliamentary Guard</a></strong>, <strong><a class="maplink" data-title="Hungarian Ministry of Justice">Hungarian Ministry of Justice</a></strong>, <strong><a class="maplink" data-title="Witness Protection Service (Hungary)">Witness Protection Service</a></strong>, <strong>the</strong> <strong><a class="maplink" data-title="National Directorate-General for Aliens Policing (Hungary)">National Directorate-General for Aliens Policing</a> and <a class="maplink" data-title="Institution of the President of the Republic (Hungary)">Institution of the President of the Republic</a>.</strong> As of September 2020 the <a class="maplink" data-title="NOVA.mobil FR in Hungary">NOVA.Mobil application</a> has been launched for police officers to identify people on the streets who do not have identity documents with them (TELEFI 2021, 86).<a href="#fnref16" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn17" role="doc-endnote"><p>Automated Fingerprint Identification System. The system, managed by the <strong><a class="maplink" data-title="Ministry of Interior (Italy)">Italian ministry of interior</a></strong> can be interrogated via a software developed by the company <strong><a class="maplink" data-title="Reco 3.26">Reco 3.26</a></strong>, a subsidiary of <strong><a class="maplink" data-title="Parsec 3.26">Parsec 3.26</a></strong>. Another software used is provided by the japanese company <strong><a class="maplink" data-title="NEC">NEC</a></strong>.<a href="#fnref17" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn18" role="doc-endnote"><p>Biometric Data Processing System (criminal data array), supported by database software from <strong><a class="maplink" data-title="RIX Technologies">RIX Technologies</a></strong>, a search engine (<a class="maplink" data-title="MorphoTrust_ABIS_Search_Engine">MorphoTrust</a>)</strong> provided by <strong><a class="maplink" data-title="IDEMIA">Idemia</a> and <a class="maplink" data-title="Safran Group">Safran Group</a></strong> managed by the <strong><a class="maplink" data-title="Ministry of Interior (Latvia)">Latvian ministry of interior</a></strong>.<a href="#fnref18" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn19" role="doc-endnote"><p><strong><a class="maplink" data-title="HDR (Lithuania)">Habitoscopic Data Register</a></strong>, managed by the <strong><a class="maplink" data-title="Ministry of Interior (Lithuania)">Ministry of Interior (Lithuania)</a></strong><a href="#fnref19" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn20" role="doc-endnote"><p>Central Automatic TeChnology for Recognition of Persons, managed by the <strong><a class="maplink" data-title="Centrum voor Biometrie">Centrum voor Biometrie</a></strong>, connected to the <strong><a class="maplink" data-title="Dutch Judicial Information Service (Justid)">Dutch Judicial Information Service (Justid)</a>.</strong><a href="#fnref20" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn21" role="doc-endnote"><p>The database uses <strong>VeriLook</strong> and <strong>Face Trace</strong> software from the Lithuanian company <strong><a class="maplink" data-title="Neurotechnology">Neurotechnology</a>. It is managed by the <strong><a class="maplink" data-title="Ministry of Interior (Slovenia)">Ministry of Interior (Slovenia)</a>.</strong><a href="#fnref21" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn22" role="doc-endnote"><p>Automated Biometric Identification System, searchable by the <strong>IntellQ</strong> software from the company <strong><a class="maplink" data-title="IntellByte">IntellByte</a>,</strong> managed by the <strong><a class="maplink" data-title="Ministry of Interior (Croatia)">Ministry of the Interior</a> (Croatia).</strong><a href="#fnref22" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn23" role="doc-endnote"><p>Central Biometric Information System<a href="#fnref23" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn24" role="doc-endnote"><p>National Biometric Identification System, managed by the <strong><a class="maplink" data-title="Ministry of Interior (Romania)">Ministry of Interior (Romania)</a></strong><a href="#fnref24" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn25" role="doc-endnote"><p>Managed by the <strong><a class="maplink" data-title="The Photographic and Graphic Laboratory of Criminalistic Services">Photographic and Graphic Laboratory of Criminalistic Services</a>,</strong> using search software by the company <strong><a class="maplink" data-title="Unidas">Unidas</a></strong><a href="#fnref25" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn26" role="doc-endnote"><p>Managed by the <strong>Estonian Ministry of Interior</strong><a href="#fnref26" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn27" role="doc-endnote"><p><a class="maplink" data-title="IFRS (Interpol)">Interpol Facial Recognition System</a><a href="#fnref27" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn28" role="doc-endnote"><p>Source: TELEFI Report p.23.<a href="#fnref28" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn29" role="doc-endnote"><p>As detailed in CHAPTER 4. However, that does not mean that it is not subjected to similar legal frameworks.<a href="#fnref29" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn30" role="doc-endnote"><p>Developed as a partnership between the <strong><a class="maplink" data-title="Dutch Ministry of Justice & Security">Dutch Ministry of Justice &amp; Security</a></strong>, the <strong><a class="maplink" data-title="Dutch Institute for Technology Safety and Security (DITSS)">Dutch Institute for Technology Safety and Security (DITSS)</a></strong>, the <strong><a class="maplink" data-title="Rotterdam Municipality">Rotterdam Municipality</a></strong>, insurance company <strong><a class="maplink" data-title="Interpolis">Interpolis</a></strong>, the <strong><a class="maplink" data-title="Dutch Police">Dutch Police</a></strong>, the <strong><a class="maplink" data-title="ViNotion">ViNotion</a></strong>, the <strong><a class="maplink" data-title="Avans Hogeschool">Avans Hogeschool</a></strong>, the <strong><a class="maplink" data-title="Munisense">Munisense</a></strong>, the <strong><a class="maplink" data-title="Sustainder">Sustainder</a></strong>, the <strong><a class="maplink" data-title="Twente University">Twente University</a></strong>, the <strong><a class="maplink" data-title="Max Planck Institute for the Study of Crime, Security and Law">Max Planck Institute for the Study of Crime, Security and Law</a></strong>, and <strong><a class="maplink" data-title="The Network Institute">The Network Institute</a> (Vrije Universiteit Amsterdam).</strong><a href="#fnref30" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn31" role="doc-endnote"><p>Developed in partnership between the <strong><a class="maplink" data-title="Dutch Institute for Technology Safety and Security (DITSS)">Dutch Institute for Technology Safety and Security (DITSS)</a></strong>, <strong><a class="maplink" data-title="Atos">Atos</a></strong>, <strong>the <a class="maplink" data-title="Municipality of Eindhoven">Municipality of Eindhoven</a></strong>, <strong><a class="maplink" data-title="Tilburg University">Tilburg University</a></strong>, <strong><a class="maplink" data-title="Eindhoven University of Technology">Eindhoven University of Technology</a></strong>, <strong><a class="maplink" data-title="Intel">Intel</a></strong>, <strong><a class="maplink" data-title="Sorama">Sorama</a></strong>, and <strong>Axis Communications</strong>; it uses search software from <strong><a class="maplink" data-title="Oddity.ai">Oddity.ai</a></strong> (a spinout of <a class="maplink" data-title="Utrecht University">Utrecht University</a>) and <strong><a class="maplink" data-title="ViNotion">ViNotion</a></strong>.<a href="#fnref31" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn32" role="doc-endnote"><p>Developed by <a class="maplink" data-title="Retevision">Retevision</a>, <a class="maplink" data-title="Instituto Tecnológico de Castilla y León (ITCL)">Instituto Tecnológico de Castilla y León (ITCL)</a>, <a class="maplink" data-title="Centro para el Desarrollo Tecnológico Industrial">Centro para el Desarrollo Tecnológico Industrial</a>, <a class="maplink" data-title="Cellnex">Cellnex</a>, <a class="maplink" href="Herta Security">Herta Security</a>, <a class="maplink" href="Sngular">Sngular</a>, <a class="maplink" href="Emergya">Emergya</a>, SHS, <a class="maplink" href="Televés">Televés</a>, <a class="maplink" href="Universidad de Granada">Universidad de Granada</a>, <a class="maplink" href="Universidad Politecnica de Madrid">Universidad Politecnica de Madrid</a>, <a class="maplink" href="Universidad Carlos III.">Universidad Carlos III</a>.<a href="#fnref32" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn33" role="doc-endnote"><p>Using the software <strong><a class="maplink" data-title="SARI (Deployment)">SARI</a></strong> by the company <a class="maplink" data-title="Parsec 3.26">Parsec 3.26</a>, developed in partnership with <strong><a class="maplink" data-title="Telecom Italia">Telecom Italia</a></strong><a href="#fnref33" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn34" role="doc-endnote"><p>The Defenseur des Droits is a governmental watchdog on civil rights and liberties in France. See Defenseur des Droits (2021) for the call for a ban on facial recognition.<a href="#fnref34" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn35" role="doc-endnote"><p>While the <strong>eID project</strong> is not specific to Belgium, the country stands out for having piloted the project ahead of other EU member states. eID is a form of authentication rather than surveillance system - yet the constitution of a database of machine-readable identities participates to the construction of a digital infrastructure of surveillance that can be misuedmisused for biometric mass surveillance., as argued in chapter 3<a href="#fnref35" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn36" role="doc-endnote"><p>The technology is provided by <a class="maplink" data-title="RTS">RTS</a>, a security technology reseller.<a href="#fnref36" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn37" role="doc-endnote"><p>The COC, or Supervisory Body for Police Information is « the autonomous federal parliamentary body in charge of monitoring the management of police information and also the data controller for the integrated police service, the Passenger Information Unit and the General Inspectorate of the Federal and the Local Police. » (Organe de Controle de l'Information Policière 2021).<a href="#fnref37" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn38" role="doc-endnote"><p><a href="https://reclaimyourface.eu/" target="_blank">https://reclaimyourface.eu/</a><a href="#fnref38" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn39" role="doc-endnote"><p>The other companies are: Arclan Systems, Business Card Associates, <a class="maplink" data-title="Deveryware">Deveryware</a>, Egidium, Gemalto, Geol Semantics, Igo, Inria, Luceor, Onhys, <a class="maplink" data-title="IDEMIA">IDEMIA</a>, Sys, Sysnav and Yncrea.<a href="#fnref39" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn40" role="doc-endnote"><p>Banque Publique d’Investissement: French Public Investment Bank<a href="#fnref40" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn41" role="doc-endnote"><p>Comité de la Filière industrielle de la sécurité<a href="#fnref41" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn42" role="doc-endnote"><p>For the campaign, see: <a href="http://www.technopolice.fr" target="_blank">http://www.technopolice.fr</a><a href="#fnref42" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn43" role="doc-endnote"><p>Image from <a href="https://netzpolitik.org/2020/deutlich-mehr-gesichtserkennung-bei-bundespolizei-und-kriminalaemtern/" target="_blank">https://netzpolitik.org/2020/deutlich-mehr-gesichtserkennung-bei-bundespolizei-und-kriminalaemtern/</a><a href="#fnref43" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn44" role="doc-endnote"><p><a href="https://youtu.be/5WD9b6tWC0Q" target="_blank">https://youtu.be/5WD9b6tWC0Q</a><a href="#fnref44" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn45" role="doc-endnote"><p><a target="_blank" href="https://edps.europa.eu/press-publications/press-news/press-releases/2021/edpb-edps-call-ban-use-ai-automated-recognition_en">https://edps.europa.eu/press-publications/press-news/press-releases/2021/edpb-edps-call-ban-use-ai-automated-recognition_en</a><a href="#fnref45" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn46" role="doc-endnote"><p><a target="_blank" href="https://edri.org/wp-content/uploads/2021/08/European-Digital-Rights-EDRi-submission-to-European-Commission-adoption-consultation-on-the-Artificial-Intelligence-Act-August-2021.pdf">https://edri.org/wp-content/uploads/2021/08/European-Digital-Rights-EDRi-submission-to-European-Commission-adoption-consultation-on-the-Artificial-Intelligence-Act-August-2021.pdf</a><a href="#fnref46" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
</ol>
</section>
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<div class="section" data-title="Dragonfly Project">
<h2>Project Dragonfly</h2>
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Nulla mattis facilisis felis, eu lacinia neque condimentum ut. Suspendisse fermentum ultrices faucibus.
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</div>
<div class='section' data-title="Burglary-Free Neighbourhood">
<h2>Burglary-free Neighbourhood</h2>
<p>
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varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.
</p>
<p>
Integer tincidunt odio non mauris egestas viverra. Nulla blandit vulputate enim vel vestibulum. Donec
suscipit dignissim nisi a scelerisque. Class aptent taciti sociosqu ad litora torquent per conubia
nostra, per inceptos himenaeos. Donec egestas mauris posuere diam tempor, vel consectetur enim rhoncus.
Phasellus accumsan feugiat dui, sit amet viverra neque facilisis in. Pellentesque fermentum ipsum
porttitor, luctus felis eu, ornare quam. Integer ut pharetra nisi. Phasellus sed facilisis quam, nec
porta metus. Sed consequat ac nisl eget faucibus. Proin blandit nisi id pharetra vestibulum. Vestibulum
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facilisis libero a, volutpat dolor. Nulla vel accumsan diam. Praesent eu neque nibh.
</p>
</div>
<p>
Suspendisse ac libero mi. In hac habitasse platea dictumst. Ut bibendum a dui eu congue. Sed et lacus
congue, faucibus nulla cursus, sagittis odio. In ac risus vehicula, interdum mauris quis, porttitor
massa. Vivamus non iaculis tortor. Mauris ac pharetra urna. Integer ac mattis enim. Integer egestas
velit purus. In hac habitasse platea dictumst. Ut interdum felis ut libero pellentesque, ac vulputate
felis faucibus. Sed facilisis fringilla leo, et condimentum quam fermentum ac. Maecenas blandit
sollicitudin libero in pretium.
</p>
<p>
Donec sodales convallis sapien eget rutrum. Nullam fermentum nisi nec auctor convallis. Phasellus
euismod ante erat, ut vulputate sem rhoncus et. Quisque ac eros sit amet metus aliquet blandit. Integer
sagittis ligula vitae orci gravida rhoncus. Vestibulum eu ante id ipsum ultricies tempor. Proin viverra,
felis vel suscipit laoreet, lorem nisl condimentum ipsum, id convallis ipsum ante ac sem. Maecenas vitae
ultricies augue. Morbi lectus dolor, tempus ac scelerisque sit amet, condimentum eget tortor. Fusce id
rutrum ligula.
</p>
<p>
In suscipit purus at leo convallis sodales. Sed a placerat arcu. Fusce id mi quis nisl ullamcorper
imperdiet. Donec eu leo sit amet lacus euismod accumsan. Fusce facilisis congue leo eu commodo. Sed vel
libero sem. Nulla nec volutpat eros. Pellentesque habitant morbi tristique senectus et netus et
malesuada fames ac turpis egestas. In lacinia massa sed justo tincidunt, sed aliquam erat tristique. Nam
porta pharetra nisi eu aliquet.
</p>
<p>
In in neque vel ligula dictum suscipit. Cras cursus a felis nec mattis. Praesent rhoncus orci nec nulla
rutrum, sit amet iaculis massa varius. Mauris sit amet finibus ipsum. Nam sit amet augue sed ipsum
laoreet venenatis. Praesent feugiat faucibus congue. Nunc vulputate ante at neque suscipit auctor. In
nec sagittis lacus. Suspendisse ornare ut eros a lobortis. Donec et pharetra urna, sit amet dictum enim.
Morbi fringilla ligula tristique nibh tempus, quis pharetra sem imperdiet. Suspendisse auctor fringilla
purus, ultricies feugiat augue consectetur et. Integer neque mauris, elementum imperdiet finibus vel,
suscipit ac metus. In hac habitasse platea dictumst. Mauris eu nibh porttitor, tempor enim vel,
tristique libero.
</p>
<p>
Suspendisse commodo in justo a hendrerit. Nunc nec laoreet ante. Nam ac tincidunt libero, sed rhoncus
libero. Nunc et arcu a ex consequat malesuada eget vel nunc. Mauris felis lectus, sodales sed lacus id,
congue blandit diam. Curabitur eu quam accumsan augue accumsan lacinia. Nulla id justo elementum,
placerat lectus et, varius purus. Nam congue, erat at congue posuere, diam felis consequat mi, a tempor
erat odio id dolor. Nunc pulvinar libero sit amet neque posuere, sit amet placerat metus facilisis. In
justo libero, facilisis condimentum molestie eu, tristique in sapien. Interdum et malesuada fames ac
ante ipsum primis in faucibus. In mattis, nisl eu mollis ultricies, justo lacus commodo nisl, ut
pulvinar augue odio nec leo. Pellentesque ornare tellus nec massa pellentesque, id euismod odio mattis.
Aliquam at massa a nisi maximus varius. Donec interdum odio id nibh commodo, in ultricies lacus lacinia.
Ut et ultricies libero.
</p>
<p>
Nunc pretium vehicula leo, ut vehicula dolor ultricies quis. Nunc ultricies elit lobortis lorem tempus
placerat. Vestibulum vitae velit ut libero pretium dignissim sed vitae sapien. Ut ornare nunc nec tortor
sodales, nec volutpat risus ornare. Morbi ac iaculis arcu. Fusce viverra feugiat porta. Nam aliquam
aliquet urna quis consequat. Sed suscipit arcu ut eros imperdiet posuere non at nunc. Quisque ac
convallis lacus, non efficitur nulla. Interdum et malesuada fames ac ante ipsum primis in faucibus.
Fusce suscipit tincidunt pharetra. Sed eget egestas enim.
</p>
<p>
Quisque tellus nulla, facilisis eget ante ac, pharetra pretium mauris. Nam arcu urna, condimentum sed
urna sed, mattis dictum nisi. Maecenas eget ante facilisis, dignissim nisl non, venenatis odio. Integer
turpis enim, sagittis eu commodo nec, mollis at ex. In non dui dictum, porttitor mauris in, rutrum
dolor. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Proin vel
nisi eros. Aliquam vel turpis est. Class aptent taciti sociosqu ad litora torquent per conubia nostra,
per inceptos himenaeos. In libero risus, feugiat non tristique quis, egestas vel tortor. Praesent cursus
lorem nec euismod placerat. Fusce tincidunt non risus at luctus. Nullam hendrerit mi diam, ut rhoncus
neque accumsan vel. Sed cursus dolor id velit ullamcorper rhoncus. Duis volutpat sodales aliquam.
</p>
<p>
Curabitur dignissim posuere ex sit amet porta. Sed viverra arcu et sapien tristique, ullamcorper
eleifend elit posuere. Aenean nec augue dui. Proin semper efficitur vehicula. Praesent vestibulum tortor
fringilla dolor congue maximus. Morbi sagittis mollis blandit. Lorem ipsum dolor sit amet, consectetur
adipiscing elit. Cras pulvinar laoreet tincidunt. Mauris hendrerit mollis neque nec vehicula. Mauris
auctor pharetra nisl, at ornare ante accumsan feugiat. Maecenas felis risus, posuere vel nunc et,
fringilla viverra dolor. Cras consectetur rutrum ipsum vel tempus. Etiam mattis ante in lacus mollis,
non porta quam pulvinar. Sed rutrum tempor placerat. Sed id dui eleifend, interdum tellus ut, sagittis
odio. Ut iaculis at enim a dignissim.
</p>
<p>
Proin tempor mi sed mattis mollis. Quisque nulla odio, rutrum vitae ligula eget, interdum faucibus
tellus. Aliquam et augue vel justo molestie vestibulum eu nec risus. Maecenas aliquet, risus eget
feugiat consequat, diam magna bibendum diam, sed pharetra tellus purus ut est. Aenean dictum pulvinar
vestibulum. Sed non neque eros. Etiam vitae aliquam nisl, quis congue velit. Ut gravida libero vel
vestibulum tincidunt. Sed vel nibh ac velit aliquam porttitor eget et nulla.
</p>
<p>
Suspendisse ut odio molestie odio auctor sollicitudin. Vestibulum viverra ornare nibh, vel lobortis odio
dapibus ac. Cras dapibus porta libero, nec congue nisi sagittis sit amet. Class aptent taciti sociosqu
ad litora torquent per conubia nostra, per inceptos himenaeos. Donec ullamcorper leo vitae urna
sollicitudin sodales. Suspendisse quis nulla vitae quam ornare lobortis sodales sit amet lectus.
Interdum et malesuada fames ac ante ipsum primis in faucibus. Ut eleifend gravida laoreet. Cras id purus
vehicula, egestas velit sit amet, pretium magna. In in pulvinar quam.
</p>
<p>
Aliquam fermentum a tellus a sodales. Pellentesque habitant morbi tristique senectus et netus et
malesuada fames ac turpis egestas. Etiam facilisis molestie ipsum ac viverra. Vestibulum lacus nunc,
egestas at lorem vel, pulvinar consequat lacus. Sed feugiat nisl vel risus vehicula, sed imperdiet elit
ornare. Maecenas non magna vitae erat feugiat scelerisque. Nam vulputate lacus porta porttitor
sollicitudin.
</p>
<p>
Quisque auctor tellus lorem, et ornare tellus efficitur id. Cras dictum tortor quis lorem facilisis, in
molestie erat porta. Sed vel porttitor libero. Etiam pulvinar augue a semper finibus. Mauris tellus
metus, lobortis non viverra eu, commodo quis nunc. Maecenas vitae pellentesque lectus. Nunc ut felis
tellus. Phasellus a mi in neque placerat egestas eget vitae tellus. Vivamus ut metus tellus.
Pellentesque mollis tempus tempus.
</p>
<p>
Ut at pretium lacus, non sollicitudin velit. Vivamus sagittis fermentum purus, nec suscipit lectus
imperdiet eu. Cras velit risus, porttitor vitae lectus id, condimentum pulvinar sem. Ut lacinia metus eu
rhoncus finibus. Vivamus accumsan, ex suscipit porttitor dictum, tellus lectus luctus lorem, sed lacinia
lectus sem et ligula. Duis vitae turpis nunc. Curabitur non sapien felis. Maecenas a egestas nunc, eget
luctus quam. Duis vestibulum metus eu turpis vulputate, et lacinia ante maximus. Vivamus molestie leo
eget sem dictum, nec posuere sapien euismod.
</p>
<p>
Cras et risus venenatis nisi dapibus feugiat non at dui. Pellentesque faucibus nulla aliquam nunc
congue, ut fringilla est viverra. Mauris nulla elit, ornare non augue a, cursus elementum diam. Proin
pulvinar justo nulla, et semper tortor dapibus ac. Maecenas ac libero ut eros hendrerit auctor at nec
augue. Aliquam dictum, ligula fermentum lobortis egestas, risus neque pulvinar felis, id lobortis tellus
nunc eget massa. Fusce vel mollis arcu. Suspendisse dapibus, tortor in blandit pulvinar, nulla dolor
euismod justo, vel ullamcorper dui ante ac dolor. Duis congue urna et purus posuere semper. Phasellus ut
feugiat justo, in commodo turpis. Donec venenatis et urna ut vestibulum.
</p>
<p>
Nam tristique odio a ultricies suscipit. Cras sollicitudin vulputate euismod. Sed elementum congue
tortor, ut pretium lacus dapibus accumsan. Sed eget aliquam tortor. Donec blandit tempor tellus, sit
amet pharetra mauris iaculis vel. Quisque pharetra nisi eget orci egestas dignissim. Mauris sed laoreet
ligula, vel placerat ante. Curabitur vel libero ornare, suscipit ante sed, feugiat orci. Sed malesuada
mattis augue. Sed auctor mattis diam, bibendum pretium arcu tempus quis.
</p>
<p>
Praesent id finibus purus, sed congue turpis. Maecenas fringilla, nisi non elementum congue, diam nunc
vestibulum urna, non elementum quam risus ut tellus. Donec imperdiet, turpis vel elementum sollicitudin,
ipsum odio faucibus lectus, quis facilisis urna nibh non ipsum. Sed sed massa sed orci cursus viverra eu
quis velit. Donec volutpat pretium erat vitae sodales. Suspendisse posuere nisi at orci rhoncus,
fermentum sodales lacus scelerisque. Suspendisse consectetur enim eget faucibus eleifend. Nullam magna
eros, elementum id aliquam non, egestas eu nunc. Sed tincidunt scelerisque nibh, id venenatis dolor
ultricies vitae. Morbi sapien nisi, tempor ut odio non, varius varius ipsum.
</p>
<p>
Pellentesque sit amet maximus nibh, et placerat orci. Duis accumsan libero a dolor dignissim viverra.
Sed purus nibh, egestas eu nisi at, commodo elementum sem. Nunc id dui a ex condimentum venenatis.
Maecenas id mattis nunc. Fusce accumsan nibh nec lacus congue consequat. Donec cursus laoreet leo in
egestas. Proin sit amet dui tristique, rhoncus tortor eget, aliquet tellus. Aenean vitae pellentesque
ex, in congue odio. Etiam lectus velit, fermentum ultricies consequat eget, pellentesque quis tellus.
</p>
<p>
Aenean id libero et turpis fringilla elementum. Orci varius natoque penatibus et magnis dis parturient
montes, nascetur ridiculus mus. Sed vel ex euismod, dignissim nunc ac, consequat ipsum. Donec et tortor
nisi. Nulla tempor turpis nec elit aliquam, id rutrum nulla blandit. Maecenas ullamcorper turpis
placerat lectus imperdiet tincidunt. Vestibulum tempor posuere risus tincidunt porttitor. Mauris sit
amet hendrerit diam, non porta sem. Proin finibus quam eu metus vulputate, quis vehicula lectus
lobortis. Aenean libero felis, ullamcorper sit amet sagittis et, scelerisque et leo. Sed vehicula sem
justo, eget pulvinar leo laoreet nec. In hac habitasse platea dictumst.
</p>
<p>
Donec ac felis sem. Morbi lorem felis, mollis ac elit ut, luctus congue augue. Donec leo purus, accumsan
non pulvinar vel, hendrerit vitae diam. Etiam bibendum ut quam vehicula cursus. Mauris tristique
volutpat ligula. Ut quis augue sollicitudin, tincidunt nisl ac, egestas turpis. Morbi gravida in nisi
non interdum. Nullam pharetra accumsan tellus in laoreet. Mauris auctor auctor odio. Nunc cursus at
metus a fermentum. Interdum et malesuada fames ac ante ipsum primis in faucibus.
</p>
<p>
Mauris hendrerit vel mi non mollis. Morbi nibh felis, convallis non lacinia sed, accumsan in nulla.
Fusce non erat in massa venenatis euismod. Fusce justo felis, varius nec dolor vel, lobortis fringilla
enim. Pellentesque ac fringilla mi. Aliquam ultrices interdum pellentesque. Vivamus purus mi, porta ut
dui malesuada, interdum lobortis leo. Sed in sem in dolor hendrerit posuere ut eget tortor. Duis id
dolor vitae risus accumsan tincidunt.
</p>
<p>
Sed consectetur ornare risus porta rutrum. Sed vel leo at neque rutrum lobortis eu at lorem. Curabitur
ultrices congue lacinia. Proin non bibendum augue. Maecenas et volutpat leo, in rutrum neque. In
efficitur, purus quis pharetra pretium, nibh metus pharetra metus, vitae euismod mi metus a nibh.
Praesent condimentum velit felis, sollicitudin pretium elit lacinia nec. Integer non arcu dignissim,
suscipit nunc nec, rutrum tellus. Mauris lobortis, lorem lacinia sagittis tincidunt, mi lacus viverra
orci, id ultricies ipsum dui at quam.
</p>
<p>
Maecenas ornare sagittis vestibulum. Phasellus egestas a tellus sit amet ullamcorper. Sed eu vestibulum
sapien. Ut et ipsum augue. Sed tincidunt nibh magna, non accumsan dolor aliquet eu. Cras vitae pharetra
elit. Donec pretium dui nec pellentesque vehicula. Cras eu sem elementum mi posuere fringilla. Duis
porta, purus a sodales rhoncus, eros eros iaculis turpis, nec ornare magna nisl a ex. Pellentesque
dictum eu mi non pretium. Donec pharetra elementum dui. Curabitur et nunc ut nunc euismod aliquet eu
quis libero. Quisque sollicitudin metus maximus elit lacinia bibendum a a velit. Sed commodo est at nisl
varius, et egestas libero mattis. Vivamus sit amet rutrum turpis, nec vulputate purus. Morbi in feugiat
massa.
</p>
<p>
Pellentesque vel justo congue, scelerisque ex at, vehicula diam. Donec elementum tincidunt nulla, et
pharetra ex varius et. Suspendisse iaculis ligula a dolor bibendum, quis eleifend velit tristique. Fusce
vitae leo vitae dolor porta lobortis. Pellentesque consectetur sem quis dolor rhoncus volutpat.
Pellentesque ullamcorper volutpat condimentum. Cras venenatis elit a vehicula feugiat. Maecenas interdum
enim justo, eu tincidunt nibh pharetra at. Cras porta dui ut accumsan aliquam. Nam ut ultricies dolor.
Ut in libero vitae nibh accumsan maximus. Duis hendrerit ligula ligula, vehicula ullamcorper purus
cursus vel. Mauris efficitur vulputate elit id fermentum. Nam turpis lorem, fermentum et ullamcorper ut,
ultrices aliquam felis. Morbi et faucibus justo.
</p>
<p>
Morbi a nisi nibh. Aenean ut ex ut justo cursus venenatis. Aliquam erat volutpat. Phasellus posuere
libero sed enim pretium maximus. Curabitur risus nunc, feugiat nec libero sed, fermentum semper turpis.
Pellentesque vestibulum scelerisque ipsum quis porttitor. Proin dapibus tortor sed odio dignissim, a
maximus turpis fringilla.
</p>
<p>
Aenean blandit nulla lorem, sodales rutrum leo dignissim ac. Aliquam ornare nulla mi, eget mollis enim
rhoncus eu. Donec quis erat sem. Sed in orci quis lectus interdum efficitur. Fusce non fringilla orci.
Pellentesque elementum ut leo at aliquet. Mauris fringilla, nisi in imperdiet consectetur, lorem tellus
iaculis eros, quis ullamcorper justo est sit amet felis. In felis justo, mattis nec elit vel, convallis
accumsan libero. Nulla facilisi. Suspendisse faucibus, magna a hendrerit tempor, elit elit posuere nibh,
non maximus mi enim ac justo. Mauris ut tempor leo. Etiam finibus imperdiet pellentesque. Vestibulum
massa arcu, iaculis vitae leo eu, fringilla semper metus. Etiam nunc dolor, consectetur eu justo non,
feugiat viverra est. Aliquam tristique molestie purus, quis ultrices est ullamcorper eu. Etiam cursus
elit et rhoncus viverra.
</p>
<p>
Donec et porttitor lacus. Suspendisse venenatis sapien ut ultrices ornare. Quisque dignissim, tortor ut
fringilla dignissim, est orci lobortis odio, ac egestas sem odio dapibus lorem. Vestibulum in magna
fermentum, laoreet metus sit amet, molestie lacus. Ut quam felis, dignissim non gravida non, vulputate
quis sapien. Sed sit amet tellus sem. Fusce eget ligula et enim feugiat vulputate. Duis nec orci id
lectus accumsan fermentum vel ac dolor. Aliquam vel accumsan augue, at consequat nibh. Ut vulputate
sapien augue, ullamcorper accumsan magna luctus id. In congue et dui sed tempus. Sed et venenatis dui,
et viverra sem. Nam elementum, libero sed lobortis pellentesque, nulla dolor tincidunt odio, in
ullamcorper orci magna eu nulla.
</p>
<p>
Etiam vitae pharetra erat. Pellentesque ullamcorper a urna vel gravida. Maecenas dapibus erat sed metus
ultrices, venenatis pulvinar felis bibendum. Morbi sollicitudin pretium rhoncus. Proin tempor pretium
feugiat. Ut sed ornare eros. Etiam sed ullamcorper justo. Integer tristique ornare lobortis. Aenean et
vestibulum sapien. Aliquam non interdum diam. Pellentesque egestas sagittis nibh, eu feugiat leo
vestibulum eget. Proin bibendum ligula sapien, ut dapibus turpis sodales sed. Sed vitae nunc feugiat,
porta diam non, fringilla arcu. Suspendisse eleifend arcu vitae mi auctor suscipit. Lorem ipsum dolor
sit amet, consectetur adipiscing elit. Integer ut odio vel ipsum luctus maximus.
</p>
<p>
Nunc rhoncus dui nibh, ac pulvinar risus vulputate in. Quisque gravida convallis ultricies. In vitae
felis dolor. Aenean congue vitae libero ac mattis. Quisque tincidunt arcu id mi bibendum, non viverra mi
vulputate. Sed mollis commodo elementum. Vivamus iaculis urna at ante tempor, eu aliquet elit bibendum.
Praesent feugiat lacinia dolor, sit amet semper enim auctor nec.
</p>
<p>
Vivamus tincidunt turpis id nulla maximus commodo. Nullam a ligula ut risus congue tempus in et ipsum.
Fusce dapibus a elit vitae sagittis. Nam metus quam, pretium a tortor eu, molestie hendrerit lacus. Cras
volutpat lacinia sem, non volutpat tellus pretium sagittis. Morbi a viverra diam, vel feugiat nisi. Orci
varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Cras varius nibh ut
tempor ullamcorper. Cras convallis, massa sit amet mollis laoreet, elit erat scelerisque arcu, ac
bibendum augue dolor vel nunc. Aliquam sit amet orci nec dui venenatis imperdiet.
</p>
<p>
Nulla facilisi. Aliquam vehicula velit in aliquam auctor. Cras ut nibh diam. Donec vitae vulputate
lacus. Suspendisse potenti. Morbi quis leo quis sapien sagittis finibus in id purus. Sed congue
fringilla purus, vel finibus leo aliquet eu. Cras volutpat sapien imperdiet erat consectetur interdum.
Nam eu pretium diam, quis mattis lacus.
</p>
<p>
Donec sed ligula nunc. Donec fermentum tempor diam, malesuada cursus ante iaculis at. Aliquam sodales
lectus in lorem molestie commodo. Ut aliquet faucibus commodo. Curabitur ut nisi sit amet orci molestie
ultrices at ac diam. Morbi nec tellus nec enim lobortis faucibus quis non tellus. Nulla varius ligula in
dolor fringilla vulputate. Phasellus sed finibus justo, nec faucibus ipsum. Cras at tempor mi. Donec
ipsum ligula, ultrices in suscipit non, cursus vel est. In vitae neque condimentum, euismod nunc at,
pulvinar est. Donec rhoncus luctus ornare.
</p>
</content>
<script>
const frameEl = document.getElementById('frame');
frameEl.addEventListener('load', function () {
const caseEls = document.getElementsByClassName('section');
for (let caseEl of caseEls) {
console.log(caseEl.dataset.title);
const toSelect = typeof caseEl.dataset.title == 'undefined' ? null : frameEl.contentWindow.getIdForTitle(caseEl.dataset.title);
// navItemEl.hash url-encodes
// let targetEl = document.getElementById(navItemEl.attributes.href.value.substr(1));
// let wrapperEl = targetEl.parentNode;
let intersectionObserver = new IntersectionObserver(function (entries) {
console.log(entries);
// If intersectionRatio is 0, the target is out of view
// and we do not need to do anything.
if (entries[0].intersectionRatio <= 0) {
// navItemEl.classList.remove('active');
} else {
if(toSelect === null) {
frameEl.contentWindow.mapGraph.deselectNode();
frameEl.contentWindow.mapGraph.resetZoom();
} else {
const node = frameEl.contentWindow.mapGraph.graph.nodes.filter(n => n.id == toSelect)[0]
frameEl.contentWindow.mapGraph.selectNode(node);
}
// navItemEl.classList.add('active');
}
});
// start observing
intersectionObserver.observe(caseEl);
}
})
// frame.contentWindow;
</script>
</body>
</html>

309
www/world_test.html
View file

@ -1,309 +0,0 @@
<html>
<head>
<style>
body{
margin: 0;
}
</style>
</head>
<body>
<script src="https://d3js.org/d3.v6.min.js"></script>
<script src="https://d3js.org/topojson.v3.min.js"></script>
<script src="//unpkg.com/d3-geo-zoom"></script>
<script type="text/javascript">
// see also: http://bl.ocks.org/dwtkns/4973620
var width = window.innerWidth;
var height = window.innerHeight;
const eu_countries = ['Austria', 'Italy', 'Belgium', 'Latvia', 'Bulgaria', 'Lithuania', 'Croatia', 'Luxembourg', 'Cyprus', 'Malta', 'Czechia', 'Netherlands', 'Denmark', 'Poland', 'Estonia ', 'Portugal', 'Finland ', 'Romania', 'France', 'Slovakia', 'Germany', 'Slovenia', 'Greece', 'Spain', 'Hungary', 'Sweden', 'Ireland'];
const config = {
'max_y_rotation' : 55,
}
var graph = {
nodes: [
{ id: "New York", lat: 40.706109, lon: -74.01194 },
{ id: "London", lat: 51.508070, lon: -0.126432 },
{ id: "Montevideo", lat: -34.901776, lon: -56.163983 },
{ id: "London-NewYork1" },
{ id: "London-NewYork2" },
{ id: "London-NewYork3" },
{ id: "Montevideo-London" }
],
links: [
{ source: "New York", target: "London-NewYork1" },
{ source: "New York", target: "London-NewYork2" },
{ source: "New York", target: "London-NewYork3" },
{ source: "London-NewYork1", target: "London" },
{ source: "London-NewYork2", target: "London" },
{ source: "London-NewYork3", target: "London" },
{ source: "London", target: "Montevideo-London" },
{ source: "Montevideo-London", target: "Montevideo" }
]
}
const force = d3.forceSimulation()
.force("link", d3.forceLink()
.id(function (d) {
return d.id;
})
.distance(10))
.force("charge", d3.forceManyBody().strength(-200));
const svg = d3.select("body")
.append("svg")
.attr("width", width)
.attr("height", height);
const container = svg.append("g").attr("id", "container");
const projection = d3.geoOrthographic()
.center([0, 0])
.translate([width / 2, height / 2])
.rotate([ -12, -52, 0])
.clipAngle(90)
.scale(height*1.5);
const proj = d3.geoPath().projection(projection);
const graticule = d3.geoGraticule10();
const g_countries = container.append("g").attr("id", "countries");
const g_borders = container.append("g").attr("id", "borders");
container.append("g")
.append('path')
.attr("class", "graticule")
.attr('d', proj(graticule))
.attr("fill", "none")
.attr("stroke-width", "!px")
.attr("stroke", (n) => {
return "lightgray";
});
;
d3.json("https://cdn.jsdelivr.net/npm/world-atlas@2/countries-110m.json").then(function (world) {
// land = topojson.feature(world, world.objects.land)
const borders = topojson.mesh(world, world.objects.countries, (a, b) => a !== b)
console.log(borders);
const countries = topojson.feature(world, world.objects.countries).features;
// console.log(topojson.feature(world, world.objects.countries).features);
g_countries.selectAll("path")
.data(countries)
.enter()
.append("path")
.attr("class", "countries")
.attr("d", proj)
.attr("fill", (n) => {
if(eu_countries.indexOf(n.properties.name) !== -1) {
return '';
}
return "lightgray";
});
g_borders//.selectAll("path")
// .data(borders)
// .enter()
.append("path")
.attr("class", "borders")
.attr("d", proj(borders))
.attr("fill", "none")
.attr("stroke-width", "2px")
.attr("stroke", (n) => {
return "white";
});
const links = container.append('g')
.attr("id", "links")
.selectAll("line")
.data(graph.links)
.enter()
.append("line")
.attr("stroke-width", 2)
.attr("stroke", "black");
const nodes = container.append('g')
.attr("id", "nodes")
.selectAll("circle")
.data(graph.nodes)
.enter()
.append("circle")
.attr('r', 5)
.call(d3.drag()
.on("start", dragInicia)
.on("drag", dragging)
.on("end", dragTermina));
force.nodes(graph.nodes);
force.force("link").links(graph.links);
graph.nodes.forEach(function (d) {
if (d.lon && d.lat) {
var p = projection([d.lon, d.lat]);
d.fx = p[0];
d.fy = p[1];
}
})
//simulación y actualizacion de la posicion de los nodos en cada "tick"
force.on("tick", function () {
links
.attr('x1', function (d) {
return d.source.x;
})
.attr('y1', function (d) {
return d.source.y;
})
.attr('x2', function (d) {
return d.target.x;
})
.attr('y2', function (d) {
return d.target.y;
})
;
nodes
.attr('cx', function (d) {
return d.x;
})
.attr('cy', function (d) {
return d.y;
})
;
})
function dragInicia(d) {
if (!d.lon || !d.lat) {
if (!d3.event.active) force.alphaTarget(0.3).restart();
d.fx = d.x;
d.fy = d.y;
}
}
function dragging(d) {
if (!d.lon || !d.lat) {
d.fx = d3.event.x;
d.fy = d3.event.y;
}
}
function dragTermina(d) {
if (!d.lon || !d.lat) {
if (!d3.event.active) force.alphaTarget(0);
d.fx = null;
d.fy = null;
}
}
function refresh() {
container.selectAll(".countries").attr("d", proj);
container.selectAll(".graticule").attr("d", proj(graticule));
container.selectAll(".borders").attr("d", proj(borders));
// console.log(graph.nodes);
force.alpha = 0;
force.restart();
graph.nodes.forEach(function (d) {
if (d.lon && d.lat) {
var p = projection([d.lon, d.lat]);
d.fx = p[0];
d.fy = p[1];
}
})
}
// svg.call(d3.zoom().extent([[0,0],[1,1]]).scaleExtent([0.3, 6]).on("start", function () {
// // svg.node().classList.add("dragging");
// }).on("end", function () {
// // svg.node().classList.remove("dragging");
// }).on("zoom", function ({ transform }) {
// // container.attr("transform", transform);
// o = [transform.x/6, -transform.y/6];
// o[1] = o[1] > config.max_y_rotation ? config.max_y_rotation :
// o[1] < -config.max_y_rotation ? -config.max_y_rotation :
// o[1];
// projection .rotate(o).scale(window.innerHeight*1.5*transform.k)
// refresh();
// // container.attr("transform", "scale(" + transform.k + ")");
// }));
// svg.on("wheel.zoom", (e) => {
// }, {passive: false})
d3.geoZoom()
.northUp(true)
.projection(projection)
.scaleExtent([.3, 6])
.onMove(refresh)(svg.node());
// (<mapDomNode>);
// d3.select('svg').call(d3.drag()
// .on("start", mousedown)
// .on("drag", mousemove)
// .on("end", mouseup));
// var m0, o0;
// function mousedown(e) {
// m0 = [d3.event.x, d3.event.y];
// o0 = projection.rotate();
// console.log(e, d3.event, m0,o0);
// // d3.event.preventDefault();
// }
// function mousemove() {
// if (m0) {
// var m1 = [d3.event.x, d3.event.y]
// , o1 = [o0[0] + (m1[0] - m0[0]) / 6, o0[1] + (m0[1] - m1[1]) / 6];
// o1[1] = o1[1] > config.max_y_rotation ? config.max_y_rotation :
// o1[1] < -config.max_y_rotation ? -config.max_y_rotation :
// o1[1];
// projection.rotate(o1);
// // sky.rotate(o1);
// refresh();
// }
// }
// function mouseup() {
// if (m0) {
// mousemove();
// m0 = null;
// }
// }
function resize(){
width = window.innerWidth;
height = window.innerHeight;
d3.selectAll('svg')
.attr("width", width)
.attr("height", height);
projection.translate([width / 2, height / 2]);
projection.scale(height*1.5);
refresh();
}
window.addEventListener('resize', resize);
});
</script>
</body>
</html>