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Any request // for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk // Licensee acknowledges receipt of notices for the Open Source Components for the initial // delivery of the Software. // * Any publications arising from the use of this software, including but // not limited to academic journal and conference publications, technical // reports and manuals, must cite at least one of the following works: // // OpenFace: an open source facial behavior analysis toolkit // Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency // in IEEE Winter Conference on Applications of Computer Vision, 2016 // // Rendering of Eyes for Eye-Shape Registration and Gaze Estimation // Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling // in IEEE International. Conference on Computer Vision (ICCV), 2015 // // Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection // Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson // in Facial Expression Recognition and Analysis Challenge, // IEEE International Conference on Automatic Face and Gesture Recognition, 2015 // // Constrained Local Neural Fields for robust facial landmark detection in the wild. // Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. // in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013. // /////////////////////////////////////////////////////////////////////////////// #ifndef __SVMDYNAMICLIN_h_ #define __SVMDYNAMICLIN_h_ #include <vector> #include <string> #include <stdio.h> #include <iostream> #include <opencv2/core/core.hpp> namespace FaceAnalysis { // Collection of linear SVR regressors for AU prediction class SVM_dynamic_lin{ public: SVM_dynamic_lin() {} // Predict the AU from HOG appearance of the face 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); // Reading in the model (or adding to it) void Read(std::ifstream& stream, const std::vector<std::string>& au_names); std::vector<std::string> GetAUNames() const { return AU_names; } private: // The names of Action Units this model is responsible for std::vector<std::string> AU_names; // For normalisation cv::Mat_<double> means; // For actual prediction cv::Mat_<double> support_vectors; cv::Mat_<double> biases; std::vector<double> pos_classes; std::vector<double> neg_classes; }; //=========================================================================== } #endif