From 9810da00e2c7354fdc178ee6d441f756f1e7c002 Mon Sep 17 00:00:00 2001 From: Tadas Baltrusaitis Date: Thu, 1 Mar 2018 20:12:44 +0000 Subject: [PATCH] Fixing readme. --- matlab_runners/Readme.txt | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/matlab_runners/Readme.txt b/matlab_runners/Readme.txt index aaf745c..080b113 100644 --- a/matlab_runners/Readme.txt +++ b/matlab_runners/Readme.txt @@ -8,38 +8,38 @@ run_demo_images.m - running the FaceLandmarkImg landmark detection on the demo i run_demo_videos.m - running the FaceTrackingVid landmark detection and tracking on prepackaged demo videos run_demo_video_multi.m - running the FaceTrackingVidMulti landmark detection and tracking on prepackaged demo videos (the difference from above is that it can deal with multiple faces) -For extracting head pose, facial landmarks, HOG features and Facial Action Units look at the following demos: +For extracting head pose, facial landmarks, HOG features, aligned faces, eye gaze, and Facial Action Units look at the following demos: feature_extraction_demo_img_seq.m - Running the FeatureExtraction project, it demonstrates how to specify parameters for extracting a number of features from a sequence of images in a folder and how to read those features into Matlab. feature_extraction_demo_vid.m - Running the FeatureExtraction project, it demonstrates how to specify parameters for extracting a number of features from a video and how to read those features into Matlab. + gaze_extraction_demo_vid.m - Example of a clip with varying gaze and extraction of eye gaze information + +The other scripts are for unit testing of the code: +- run_demo_align_size.m +- run_tes_img_seq.m The demos are configured to use CLNF patch experts trained on in-the-wild and Multi-PIE datasets, it is possible to uncomment other model file definitions in the scripts to run them instead. ======================== Head Pose Experiments ============================ To run them you will need to have the appropriate datasets and to change the dataset locations. -run_clm_head_pose_tests_svr.m - runs CLM, and CLM-Z on the 3 head pose datasets (Boston University, Biwi Kinect, and ICT-3DHP you need to acquire the datasets yourself) -run_clm_head_pose_tests_clnf.m - runs CLNF on the 3 head pose datasets (Boston University, Biwi Kinect, and ICT-3DHP you need to acquire the datasets yourself) +run_head_pose_tests_OpenFace.m - runs CLNF on the 3 head pose datasets (Boston University, Biwi Kinect, and ICT-3DHP you need to acquire the datasets yourself) ======================== Feature Point Experiments ============================ -run_clm_feature_point_tests_wild.m runs CLM and CLNF on the in the wild face datasets acquired from http://ibug.doc.ic.ac.uk/resources/300-W/ -The code uses the already defined bounding boxes of faces (these are produced using the 'ExtractBoundingBoxes.m' script on the in the wild datasets). The code relies on there being a .txt file of the same name as the image containing the bounding box. (Note that if the bounding box is not provided the code will use OpenCV Viola-Jones detector) +run_OpenFace_feature_point_tests_300W.m runs CLM and CLNF on the in the wild face datasets acquired from http://ibug.doc.ic.ac.uk/resources/300-W/ +The code uses the already defined bounding boxes of faces (these are produced using the 'ExtractBoundingBoxes.m' script on the in the wild datasets). The code relies on there being a .txt file of the same name as the image containing the bounding box in the appropriate directory, see https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments for details. -To run the code you will need to download the 300-W challenge datasets and run the bounding box extraction script, then replace the database_root with the dataset location. +To run the code you will need to download the 300-W challenge datasets and then replace the database_root with the dataset location. This script also includes code to draw a graph displaying error curves of the CLNF and CLM methods trained on in the wild data. For convenient comparisons to other state-of-art approaches it also includes results of using the following approaches on the 300-W datasets: -Xiangxin Zhu and Deva Ramanan, Face detection, pose estimation, and landmark localization in the wild, IEEE Conference on Computer Vision and Pattern Recognition, 2012 -Xuehan Xiong and Fernando De la Torre, Supervised Descent Method and its Applications to Face Alignment, IEEE Conference on Computer Vision and Pattern Recognition, 2013 -Akshay Asthana, Stefanos Zafeiriou, Shiyang Cheng, and Maja Pantic, Robust Discriminative Response Map Fitting with Constrained Local Models,IEEE Conference on Computer Vision and Pattern Recognition, 2013 - run_yt_dataset.m run the CLNF model on the YTCeleb Database (https://sites.google.com/site/akshayasthana/Annotations), you need to get the dataset yourself though. ======================== Action Unit Experiments ============================ -Evaluating our Facial Action Unit detection system on DISFA, FERA2011 and SEMAINE datasets. +Evaluating our Facial Action Unit detection system on Bosphorus, BP4D, DISFA, FERA2011 and SEMAINE datasets. As the models were partially trained/validated on DISFA, FERA2011, BP4D, UNBC, Bosphorus, and SEMAINE datasets the results might not generalise across datasets. However, this demonstrates how AU prediction can be done with our system.