A lot of changes:

- New AU recognition models trained on extra datasets - Bosphorus, UNBC, FERA2011
- Cleaner and clearer separation of static and dynamic AU models
- AU training code cleaned up and instructions added
- bug fixes with median feature computation
- AU prediction correction (smoothing and shifting) with post processing

.gitignore

@@ -28,3 +28,7 @@ exe/FeatureExtraction/out_bp4d/
 x64/Debug/
 matlab_runners/Action Unit Experiments/out_unbc/
 matlab_runners/Action Unit Experiments/out_bosph/
+matlab_runners/Action Unit Experiments/out_DISFA/
+matlab_runners/Action Unit Experiments/out_fera/
+matlab_runners/Demos/output_features_seq/
+matlab_runners/Demos/output_features_vid/

exe/FeatureExtraction/FeatureExtraction.cpp

@@ -324,12 +324,9 @@ int main (int argc, char **argv)
 	
 	// Used for image masking
 
-	cv::Mat_<int> triangulation;//TODO rem?
 	string tri_loc;
 	if(boost::filesystem::exists(path("model/tris_68_full.txt")))
 	{
-		std::ifstream triangulation_file("model/tris_68_full.txt");
-		LandmarkDetector::ReadMat(triangulation_file, triangulation);
 		tri_loc = "model/tris_68_full.txt";
 	}
 	else
@@ -337,12 +334,7 @@ int main (int argc, char **argv)
 		path loc = path(arguments[0]).parent_path() / "model/tris_68_full.txt";
 		tri_loc = loc.string();
 
-		if(exists(loc))
-		{
-			std::ifstream triangulation_file(loc.string());
-			LandmarkDetector::ReadMat(triangulation_file, triangulation);
-		}
-		else
+		if(!exists(loc))
 		{
 			cout << "Can't find triangulation files, exiting" << endl;
 			return 0;

lib/local/FaceAnalyser/AU_predictors/AU_all_best.txt

@@ -16,3 +16,20 @@ svm_combined/AU_25_dynamic.dat AU25
 svm_combined/AU_26_dynamic.dat AU26
 svm_combined/AU_28_static.dat AU28
 svm_combined/AU_45_dynamic.dat AU45
+svr_combined/AU_1_dynamic_intensity_comb.dat AU01
+svr_combined/AU_2_dynamic_intensity_comb.dat AU02
+svr_combined/AU_4_static_intensity_comb.dat AU04
+svr_combined/AU_5_dynamic_intensity.dat AU05
+svr_combined/AU_6_static_intensity_comb.dat AU06
+svr_combined/AU_7_static_intensity_comb.dat AU07
+svr_combined/AU_9_dynamic_intensity.dat AU09
+svr_combined/AU_10_static_intensity_comb.dat AU10
+svr_combined/AU_12_static_intensity_comb.dat AU12
+svr_combined/AU_14_static_intensity.dat AU14
+svr_combined/AU_15_dynamic_intensity_comb.dat AU15
+svr_combined/AU_17_dynamic_intensity_comb.dat AU17
+svr_combined/AU_20_dynamic_intensity.dat AU20
+svr_combined/AU_23_dynamic_intensity_comb.dat AU23
+svr_combined/AU_25_dynamic_intensity_comb.dat AU25
+svr_combined/AU_26_dynamic_intensity_comb.dat AU26
+svr_combined/AU_45_dynamic_intensity_comb.dat AU45

lib/local/FaceAnalyser/AU_predictors/AU_all_static.txt

@@ -16,7 +16,7 @@ svm_combined/AU_25_static.dat AU25
 svm_combined/AU_26_static.dat AU26
 svm_combined/AU_28_static.dat AU28
 svm_combined/AU_45_static.dat AU45
-svr_combined/AU_1_static_intensity_comb.dat AU01
+svr_combined/AU_1_static_intensity.dat AU01
 svr_combined/AU_2_static_intensity_comb.dat AU02
 svr_combined/AU_4_static_intensity_comb.dat AU04
 svr_combined/AU_5_static_intensity.dat AU05

lib/local/FaceAnalyser/include/FaceAnalyser.h

@@ -124,7 +124,7 @@ public:
 
 	// Identify if models are static or dynamic (useful for correction and shifting)
 	std::vector<bool> GetDynamicAUClass() const; // Presence
-	std::vector<bool> GetDynamicAUReg() const; // Intensity
+	std::vector<std::pair<string, bool>> FaceAnalyser::GetDynamicAUReg() const; // Intensity
 
 
 	void ExtractAllPredictionsOfflineReg(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic);

lib/local/FaceAnalyser/include/SVR_dynamic_lin_regressors.h

@@ -89,6 +89,11 @@ public:
 		return AU_names;
 	}
 
+	std::vector<double> GetCutoffs() const
+	{
+		return cutoffs;		
+	}
+
 private:
 
 	// The names of Action Units this model is responsible for
@@ -101,6 +106,9 @@ private:
 	cv::Mat_<double> support_vectors;	
 	cv::Mat_<double> biases;
 
+	// For AU callibration (see the OpenFace paper)
+	std::vector<double> cutoffs;
+
 };
   //===========================================================================
 }

lib/local/FaceAnalyser/src/FaceAnalyser.cpp

@@ -92,12 +92,11 @@ FaceAnalyser::FaceAnalyser(vector<cv::Vec3d> orientation_bins, double scale, int
 	align_height = height;
 
 	// Initialise the histograms that will represent bins from 0 - 1 (as HoG values are only stored as those)
-	// Set the number of bins for the histograms
-	num_bins_hog = 600;
+	num_bins_hog = 1000;
 	max_val_hog = 1;
-	min_val_hog = 0;
+	min_val_hog = -0.005;
 
-	// The geometry histogram ranges from -3 to 3
+	// The geometry histogram ranges from -60 to 60
 	num_bins_geom = 10000;
 	max_val_geom = 60;
 	min_val_geom = -60;
@@ -186,19 +185,19 @@ std::vector<bool> FaceAnalyser::GetDynamicAUClass() const
 	return au_dynamic_class;
 }
 
-std::vector<bool> FaceAnalyser::GetDynamicAUReg() const
+std::vector<std::pair<string, bool>> FaceAnalyser::GetDynamicAUReg() const
 {
-	std::vector<bool> au_dynamic_reg;
+	std::vector<std::pair<string, bool>> au_dynamic_reg;
 	std::vector<std::string> au_reg_names_stat = AU_SVR_static_appearance_lin_regressors.GetAUNames();
 	std::vector<std::string> au_reg_names_dyn = AU_SVR_dynamic_appearance_lin_regressors.GetAUNames();
 
 	for (size_t i = 0; i < au_reg_names_stat.size(); ++i)
 	{
-		au_dynamic_reg.push_back(false);
+		au_dynamic_reg.push_back(std::pair<string, bool>(au_reg_names_stat[i], false));
 	}
 	for (size_t i = 0; i < au_reg_names_dyn.size(); ++i)
 	{
-		au_dynamic_reg.push_back(true);
+		au_dynamic_reg.push_back(std::pair<string, bool>(au_reg_names_dyn[i], true));
 	}
 
 	return au_dynamic_reg;
@@ -396,11 +395,23 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 	bool update_median = true;
 
 	// TODO test if this would be useful or not
-	//if(!this->AU_predictions.empty())
+	//if(!this->AU_predictions_reg.empty())
 	//{
-	//	for(size_t i = 0; i < this->AU_predictions.size(); ++i)
+	//	vector<pair<string, bool>> dyns = this->GetDynamicAUReg();
+
+	//	for(size_t i = 0; i < this->AU_predictions_reg.size(); ++i)
 	//	{
-	//		if(this->AU_predictions[i].second > 1)
+	//		bool stat = false;
+	//		for (size_t n = 0; n < dyns.size(); ++n)
+	//		{
+	//			if (dyns[n].first.compare(AU_predictions_reg[i].first) == 0)
+	//			{
+	//				stat = !dyns[i].second;
+	//			}
+	//		}
+
+	//		// If static predictor above 1.5 assume it's not a neutral face
+	//		if(this->AU_predictions_reg[i].second > 1.5 && stat)
 	//		{
 	//			update_median = false;				
 	//			break;
@@ -417,7 +428,9 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 	if(frames_tracking % 2 == 1)
 	{
 		UpdateRunningMedian(this->hog_desc_hist[orientation_to_use], this->hog_hist_sum[orientation_to_use], this->hog_desc_median, hog_descriptor, update_median, this->num_bins_hog, this->min_val_hog, this->max_val_hog);
+		this->hog_desc_median.setTo(0, this->hog_desc_median < 0);
 	}	
+
 	// Geom descriptor and its median
 	geom_descriptor_frame = clnf_model.params_local.t();
 	
@@ -626,6 +639,7 @@ void FaceAnalyser::PostprocessPredictions()
 				{
 					// Find the appropriate AU (if not found add it)		
 					AU_predictions_reg_all_hist[AU_predictions_reg[au].first][all_ind] = AU_predictions_reg[au].second;
+
 				}
 
 				auto AU_predictions_class = PredictCurrentAUsClass(views[success_ind]);
@@ -660,6 +674,9 @@ void FaceAnalyser::ExtractAllPredictionsOfflineReg(vector<std::pair<std::string,
 	confidences = this->confidences;
 	successes = this->valid_preds;
 	
+	vector<string> dyn_au_names = AU_SVR_dynamic_appearance_lin_regressors.GetAUNames();
+
+	// Allow these AUs to be person calirated based on expected number of neutral frames (learned from the data)
 	for(auto au_iter = AU_predictions_reg_all_hist.begin(); au_iter != AU_predictions_reg_all_hist.end(); ++au_iter)
 	{
 		vector<double> au_good;
@@ -685,8 +702,30 @@ void FaceAnalyser::ExtractAllPredictionsOfflineReg(vector<std::pair<std::string,
 		else
 		{
 			std::sort(au_good.begin(), au_good.end());
-			offsets.push_back(au_good.at((int)au_good.size() / 4));
+			// If it is a dynamic AU regressor we can also do some prediction shifting to make it more accurate
+			// The shifting proportion is learned and is callen cutoff
+
+			// Find the current id of the AU and the corresponding cutoff
+			int au_id = -1;
+			for (int a = 0; a < dyn_au_names.size(); ++a)
+			{
+				if (au_name.compare(dyn_au_names[a]) == 0)
+				{
+					au_id = a;
 				}
+			}
+
+			if (au_id != -1 && AU_SVR_dynamic_appearance_lin_regressors.GetCutoffs()[au_id] != -1)
+			{
+				double cutoff = AU_SVR_dynamic_appearance_lin_regressors.GetCutoffs()[au_id];
+				offsets.push_back(au_good.at((int)au_good.size() * cutoff));				
+			}
+			else
+			{
+				offsets.push_back(0);
+			}
+		}
+		
 		aus_valid.push_back(au_good);
 	}
 	
@@ -728,7 +767,7 @@ void FaceAnalyser::ExtractAllPredictionsOfflineReg(vector<std::pair<std::string,
 		for (size_t i = (window_size - 1) / 2; i < au_iter->second.size() - (window_size - 1) / 2; ++i)
 		{
 			double sum = 0;
-			for (int w = -(window_size - 1) / 2; w < (window_size - 1) / 2; ++w)
+			for (int w = -(window_size - 1) / 2; w <= (window_size - 1) / 2; ++w)
 			{
 				sum += au_vals_tmp[i + w];
 			}
@@ -739,7 +778,6 @@ void FaceAnalyser::ExtractAllPredictionsOfflineReg(vector<std::pair<std::string,
 
 	}
 
-
 }
 
 void FaceAnalyser::ExtractAllPredictionsOfflineClass(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic)
@@ -763,7 +801,7 @@ void FaceAnalyser::ExtractAllPredictionsOfflineClass(vector<std::pair<std::strin
 		for (size_t i = (window_size - 1)/2; i < au_vals.size() - (window_size - 1) / 2; ++i)
 		{
 			double sum = 0;
-			for (int w = -(window_size - 1) / 2; w < (window_size - 1) / 2; ++w)
+			for (int w = -(window_size - 1) / 2; w <= (window_size - 1) / 2; ++w)
 			{
 				sum += au_vals_tmp[i + w];
 			}
@@ -856,7 +894,6 @@ void FaceAnalyser::UpdateRunningMedian(cv::Mat_<unsigned int>& histogram, int& h
 		converted_descriptor.setTo(cv::Scalar(num_bins-1), converted_descriptor > num_bins - 1);
 		converted_descriptor.setTo(cv::Scalar(0), converted_descriptor < 0);
 
-		// Only count the median till a certain number of frame seen?
 		for(int i = 0; i < histogram.rows; ++i)
 		{
 			int index = (int)converted_descriptor.at<double>(i);
@@ -883,9 +920,9 @@ void FaceAnalyser::UpdateRunningMedian(cv::Mat_<unsigned int>& histogram, int& h
 			for(int j = 0; j < histogram.cols; ++j)
 			{
 				cummulative_sum += histogram.at<unsigned int>(i, j);
-				if(cummulative_sum > cutoff_point)
+				if(cummulative_sum >= cutoff_point)
 				{
-					median.at<double>(i) = min_val + j * (length/num_bins) + (0.5*(length)/num_bins);
+					median.at<double>(i) = min_val + ((double)j) * (length/((double)num_bins)) + (0.5*(length)/ ((double)num_bins));
 					break;
 				}
 			}
@@ -953,7 +990,7 @@ vector<pair<string, double>> FaceAnalyser::PredictCurrentAUs(int view)
 		vector<string> svr_lin_dyn_aus;
 		vector<double> svr_lin_dyn_preds;
 
-		AU_SVR_dynamic_appearance_lin_regressors.Predict(svr_lin_dyn_preds, svr_lin_dyn_aus, hog_desc_frame, geom_descriptor_frame,  this->hog_desc_median, this->geom_descriptor_frame);
+		AU_SVR_dynamic_appearance_lin_regressors.Predict(svr_lin_dyn_preds, svr_lin_dyn_aus, hog_desc_frame, geom_descriptor_frame,  this->hog_desc_median, this->geom_descriptor_median);
 
 		for(size_t i = 0; i < svr_lin_dyn_preds.size(); ++i)
 		{
@@ -996,7 +1033,6 @@ vector<pair<string, double>> FaceAnalyser::CorrectOnlineAUs(std::vector<std::pai
 		for(size_t i = 0; i < predictions.size(); ++i)
 		{
 			// First establish presence (assume it is maximum as we have not seen max) 
-			// TODO this could be more robust by removing some outliers, or by doing it only for certain AUs?
 			if(predictions[i].second > 1)
 			{
 				double scaling_curr = 5.0 / predictions[i].second;

lib/local/FaceAnalyser/src/Face_utils.cpp

@@ -249,7 +249,7 @@ namespace FaceAnalysis
 
 		for(size_t i = 0; i < aligned_face_channels.size(); ++i)
 		{
-			aligned_face_channels[i] = aligned_face_channels[i].mul(paw.pixel_mask);
+			cv::multiply(aligned_face_channels[i], paw.pixel_mask, aligned_face_channels[i], 1.0, CV_8U);
 		}
 
 		if(aligned_face.channels() == 3)

lib/local/FaceAnalyser/src/SVR_dynamic_lin_regressors.cpp

@@ -65,6 +65,12 @@ using namespace FaceAnalysis;
 void SVR_dynamic_lin_regressors::Read(std::ifstream& stream, const std::vector<std::string>& au_names)
 {
 	
+	// For person specific calibration in a video
+	double cutoff;
+	stream.read((char*)&cutoff, 8);
+	cutoffs.push_back(cutoff);
+
+	// The feature normalization using the mean
 	if(this->means.empty())
 	{
 		LandmarkDetector::ReadMatBin(stream, this->means);
@@ -115,7 +121,6 @@ void SVR_dynamic_lin_regressors::Predict(std::vector<double>& predictions, std::
 {
 	if(AU_names.size() > 0)
 	{
-
 		cv::Mat_<double> preds;
 		if(fhog_descriptor.cols ==  this->means.cols)
 		{

lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp

@@ -632,12 +632,6 @@ bool CLNF::DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &
 					// Do the actual landmark detection
 					hierarchical_models[part_model].DetectLandmarks(image, depth, hierarchical_params[part_model]);
 
-					// Reincorporate the models into main tracker
-					for (size_t mapping_ind = 0; mapping_ind < mappings.size(); ++mapping_ind)
-					{
-						detected_landmarks.at<double>(mappings[mapping_ind].first) = hierarchical_models[part_model].detected_landmarks.at<double>(mappings[mapping_ind].second);
-						detected_landmarks.at<double>(mappings[mapping_ind].first + pdm.NumberOfPoints()) = hierarchical_models[part_model].detected_landmarks.at<double>(mappings[mapping_ind].second + hierarchical_models[part_model].pdm.NumberOfPoints());
-					}
 				}
 				else
 				{
@@ -650,9 +644,28 @@ bool CLNF::DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &
 		// Recompute main model based on the fit part models
 		if(parts_used)
 		{
+
+			for (int part_model = 0; part_model < hierarchical_models.size(); ++part_model)
+			{
+				vector<pair<int, int>> mappings = this->hierarchical_mapping[part_model];
+
+				if (!((hierarchical_model_names[part_model].compare("right_eye_28") == 0 ||
+					hierarchical_model_names[part_model].compare("left_eye_28") == 0)
+					&& !params.track_gaze))
+				{
+					// Reincorporate the models into main tracker
+					for (size_t mapping_ind = 0; mapping_ind < mappings.size(); ++mapping_ind)
+					{
+						detected_landmarks.at<double>(mappings[mapping_ind].first) = hierarchical_models[part_model].detected_landmarks.at<double>(mappings[mapping_ind].second);
+						detected_landmarks.at<double>(mappings[mapping_ind].first + pdm.NumberOfPoints()) = hierarchical_models[part_model].detected_landmarks.at<double>(mappings[mapping_ind].second + hierarchical_models[part_model].pdm.NumberOfPoints());
+					}
+				}
+			}
+
 			pdm.CalcParams(params_global, params_local, detected_landmarks);		
 			pdm.CalcShape2D(detected_landmarks, params_local, params_global);
 		}
+
 	}
 
 	// Check detection correctness

lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp

@@ -384,7 +384,7 @@ void get_image_input_output_params(vector<string> &input_image_files, vector<str
 			path image_loc(input_image_files[i]);
 
 			path fname = image_loc.filename();
-			fname = fname.replace_extension("jpg");
+			fname = fname.replace_extension("bmp");
 			output_image_files.push_back(out_img_dir + "/" + fname.string());
 			
 		}

matlab_runners/Action Unit Experiments/BP4D_valid_res_class.txt

@@ -1,11 +0,0 @@
-AU1 class, Precision - 0.470, Recall - 0.527, F1 - 0.497
-AU2 class, Precision - 0.371, Recall - 0.376, F1 - 0.373
-AU4 class, Precision - 0.422, Recall - 0.570, F1 - 0.485
-AU6 class, Precision - 0.845, Recall - 0.698, F1 - 0.765
-AU7 class, Precision - 0.719, Recall - 0.766, F1 - 0.742
-AU10 class, Precision - 0.811, Recall - 0.801, F1 - 0.806
-AU12 class, Precision - 0.902, Recall - 0.780, F1 - 0.837
-AU14 class, Precision - 0.513, Recall - 0.874, F1 - 0.647
-AU15 class, Precision - 0.406, Recall - 0.431, F1 - 0.418
-AU17 class, Precision - 0.638, Recall - 0.615, F1 - 0.626
-AU23 class, Precision - 0.357, Recall - 0.507, F1 - 0.419