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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 __LANDMARK_DETECTION_VALIDATOR_h_ #define __LANDMARK_DETECTION_VALIDATOR_h_ // OpenCV includes #include <opencv2/core/core.hpp> // System includes #include <vector> // Local includes #include "PAW.h" using namespace std; namespace LandmarkDetector { //=========================================================================== // // Checking if landmark detection was successful using an SVR regressor // Using multiple validators trained add different views // The regressor outputs -1 for ideal alignment and 1 for worst alignment //=========================================================================== class DetectionValidator { public: // What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net, 3 - new version of convolutional neural net int validator_type; // The orientations of each of the landmark detection validator vector<cv::Vec3d> orientations; // Piecewise affine warps to the reference shape (per orientation) vector<PAW> paws; //========================================== // Linear SVR // SVR biases vector<double> bs; // SVR weights vector<cv::Mat_<double> > ws; //========================================== // Neural Network // Neural net weights vector<vector<cv::Mat_<double> > > ws_nn; // What type of activation or output functions are used // 0 - sigmoid, 1 - tanh_opt, 2 - ReLU vector<int> activation_fun; vector<int> output_fun; //========================================== // Convolutional Neural Network // CNN layers for each view // view -> layer -> input maps -> kernels vector<vector<vector<vector<cv::Mat_<float> > > > > cnn_convolutional_layers; // Bit ugly with so much nesting, but oh well vector<vector<vector<vector<pair<int, cv::Mat_<double> > > > > > cnn_convolutional_layers_dft; vector<vector<vector<float > > > cnn_convolutional_layers_bias; vector< vector<int> > cnn_subsampling_layers; vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_weights; vector< vector<float > > cnn_fully_connected_layers_bias; // OLD CNN: 0 - convolutional, 1 - subsampling, 2 - fully connected // NEW CNN: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 - fully connected, 3 - relu, 4 - sigmoid vector<vector<int> > cnn_layer_types; // Extra params for the new CNN vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_biases; //========================================== // Normalisation for face validation vector<cv::Mat_<double> > mean_images; vector<cv::Mat_<double> > standard_deviations; // Default constructor DetectionValidator(){;} // Copy constructor DetectionValidator(const DetectionValidator& other); // Given an image, orientation and detected landmarks output the result of the appropriate regressor double Check(const cv::Vec3d& orientation, const cv::Mat_<uchar>& intensity_img, cv::Mat_<double>& detected_landmarks); // Reading in the model void Read(string location); // Getting the closest view center based on orientation int GetViewId(const cv::Vec3d& orientation) const; private: // The actual regressor application on the image // Support Vector Regression (linear kernel) double CheckSVR(const cv::Mat_<double>& warped_img, int view_id); // Feed-forward Neural Network double CheckNN(const cv::Mat_<double>& warped_img, int view_id); // Convolutional Neural Network double CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id); // Convolutional Neural Network double CheckCNN(const cv::Mat_<double>& warped_img, int view_id); // Convolutional Neural Network double CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id); // A normalisation helper void NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id); }; } #endif