87 lines
3 KiB
C++
87 lines
3 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
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// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
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// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
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//
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// License can be found in OpenFace-license.txt
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//
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// * Any publications arising from the use of this software, including but
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// not limited to academic journal and conference publications, technical
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// reports and manuals, must cite at least one of the following works:
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//
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// OpenFace: an open source facial behavior analysis toolkit
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
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// in IEEE Winter Conference on Applications of Computer Vision, 2016
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
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// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
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//
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///////////////////////////////////////////////////////////////////////////////
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#ifndef __SVMDYNAMICLIN_h_
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#define __SVMDYNAMICLIN_h_
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#include <vector>
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#include <string>
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#include <stdio.h>
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#include <iostream>
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#include <fstream>
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#include <opencv2/core/core.hpp>
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namespace FaceAnalysis
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{
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// Collection of linear SVR regressors for AU prediction
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class SVM_dynamic_lin{
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public:
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SVM_dynamic_lin()
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{}
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// Predict the AU from HOG appearance of the face
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void Predict(std::vector<double>& predictions, std::vector<std::string>& names, const cv::Mat_<double>& fhog_descriptor, const cv::Mat_<double>& geom_params, const cv::Mat_<double>& running_median, const cv::Mat_<double>& running_median_geom);
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// Reading in the model (or adding to it)
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void Read(std::ifstream& stream, const std::vector<std::string>& au_names);
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std::vector<std::string> GetAUNames() const
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{
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return AU_names;
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}
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private:
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// The names of Action Units this model is responsible for
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std::vector<std::string> AU_names;
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// For normalisation
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cv::Mat_<double> means;
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// For actual prediction
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cv::Mat_<double> support_vectors;
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cv::Mat_<double> biases;
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std::vector<double> pos_classes;
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std::vector<double> neg_classes;
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};
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//===========================================================================
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}
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#endif
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