diff --git a/.gitignore b/.gitignore
index 071561ae..564feb53 100644
--- a/.gitignore
+++ b/.gitignore
@@ -62,3 +62,4 @@ matlab_runners/Action Unit Experiments/out_DISFA/
 matlab_runners/Action Unit Experiments/out_fera/
 matlab_runners/Action Unit Experiments/out_SEMAINE/
 matlab_runners/Gaze Experiments/mpii_out/
+*.vspx
diff --git a/lib/local/LandmarkDetector/include/CEN_patch_expert.h b/lib/local/LandmarkDetector/include/CEN_patch_expert.h
index bf4f86b8..145ec207 100644
--- a/lib/local/LandmarkDetector/include/CEN_patch_expert.h
+++ b/lib/local/LandmarkDetector/include/CEN_patch_expert.h
@@ -81,6 +81,9 @@ namespace LandmarkDetector
 		// Faster version of the response that only considers a subset of the area_of_interest
 		void ResponseSparse(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response, cv::Mat_<float>& mapMatrix);
 
+		void ResponseSparse_mirror(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response, cv::Mat_<float>& mapMatrix);
+
+		void ResponseSparse_mirror_joint(const cv::Mat_<float> &area_of_interest_left, const cv::Mat_<float> &area_of_interest_right, cv::Mat_<float> &response_left, cv::Mat_<float> &response_right, cv::Mat_<float>& mapMatrix);
 
 	};
 
diff --git a/lib/local/LandmarkDetector/include/Patch_experts.h b/lib/local/LandmarkDetector/include/Patch_experts.h
index 582aa984..80e0bde4 100644
--- a/lib/local/LandmarkDetector/include/Patch_experts.h
+++ b/lib/local/LandmarkDetector/include/Patch_experts.h
@@ -76,6 +76,9 @@ public:
 	// Landmark visibilities for each scale and view
     vector<vector<cv::Mat_<int> > >          visibilities;
 
+	cv::Mat_<int>							mirror_inds;
+	cv::Mat_<int>							mirror_views;
+
 	// Early termination calibration values, useful for CE-CLM model to speed up the multi-hypothesis setup
 	vector<double> early_term_weights;
 	vector<double> early_term_biases;
diff --git a/lib/local/LandmarkDetector/model/cen_general.txt b/lib/local/LandmarkDetector/model/cen_general.txt
index f76e85c9..1249c1d4 100644
--- a/lib/local/LandmarkDetector/model/cen_general.txt
+++ b/lib/local/LandmarkDetector/model/cen_general.txt
@@ -1,7 +1,7 @@
-PDM pdms/pdm_68_aligned_menpo.txt
+PDM pdms/In-the-wild_aligned_PDM_68.txt
 Triangulations tris_68.txt
-PatchesCEN patch_experts/cen_patches_0.25_menpo.dat
-PatchesCEN patch_experts/cen_patches_0.35_menpo.dat
-PatchesCEN patch_experts/cen_patches_0.50_menpo.dat
-PatchesCEN patch_experts/cen_patches_1.00_menpo.dat
-EarlyTermination early_term_cen.txt
\ No newline at end of file
+PatchesCEN patch_experts/cen_patches_0.25_of.dat
+PatchesCEN patch_experts/cen_patches_0.35_of.dat
+PatchesCEN patch_experts/cen_patches_0.50_of.dat
+PatchesCEN patch_experts/cen_patches_1.00_of.dat
+EarlyTermination early_term_cen_of.txt
\ No newline at end of file
diff --git a/lib/local/LandmarkDetector/model/early_term_cen.txt b/lib/local/LandmarkDetector/model/early_term_cen.txt
deleted file mode 100644
index ea239e66..00000000
--- a/lib/local/LandmarkDetector/model/early_term_cen.txt
+++ /dev/null
@@ -1 +0,0 @@
-1.000 0.954 0.736 0.903 0.903 0.736 0.954 0.000 0.072 -0.279 -0.380 -0.380 -0.279 0.072 -0.270 -0.230 0.070 0.210 0.210 0.070 -0.230 
\ No newline at end of file
diff --git a/lib/local/LandmarkDetector/model/early_term_cen_of.txt b/lib/local/LandmarkDetector/model/early_term_cen_of.txt
new file mode 100644
index 00000000..f7c39aca
--- /dev/null
+++ b/lib/local/LandmarkDetector/model/early_term_cen_of.txt
@@ -0,0 +1 @@
+1.000 0.969 1.017 0.954 0.954 1.017 0.969 0.000 0.047 0.164 -0.375 -0.375 0.164 0.047 -0.270 -0.260 -0.170 0.250 0.250 -0.170 -0.260 
\ No newline at end of file
diff --git a/lib/local/LandmarkDetector/src/CEN_patch_expert.cpp b/lib/local/LandmarkDetector/src/CEN_patch_expert.cpp
index 69bba2aa..4d4c62d1 100644
--- a/lib/local/LandmarkDetector/src/CEN_patch_expert.cpp
+++ b/lib/local/LandmarkDetector/src/CEN_patch_expert.cpp
@@ -108,10 +108,8 @@ void CEN_patch_expert::Read(ifstream &stream)
 
 	if (num_layers == 0)
 	{
-		// empty patch due to landmark being invisible at that orientation
-
-		// read an empty int (due to the way things were written out)
-		stream.read((char*)&num_layers, 4);
+		// empty patch due to landmark being invisible at that orientation (or visible through mirroring)
+		stream.read((char*)&confidence, 8);
 		return;
 	}
 
@@ -573,3 +571,83 @@ void CEN_patch_expert::ResponseSparse(const cv::Mat_<float> &area_of_interest, c
 	response = response.reshape(1, response_height);
 	response = response.t();
 }
+
+//===========================================================================
+void CEN_patch_expert::ResponseSparse_mirror(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response, cv::Mat_<float>& mapMatrix)
+{
+
+	int response_height = area_of_interest.rows - height + 1;
+	int response_width = area_of_interest.cols - width + 1;
+
+	cv::flip(area_of_interest, area_of_interest, 1);
+
+	// Extract im2col but in a sparse way and contrast normalize
+	im2colBiasSparseContrastNorm(area_of_interest, width, height, response);
+	response = response.t();
+
+	for (size_t layer = 0; layer < activation_function.size(); ++layer)
+	{
+
+		// We are performing response = weights[layers] * response(t), but in OpenBLAS as that is significantly quicker than OpenCV		
+		cv::Mat_<float> resp = response;
+		float* m1 = (float*)resp.data;
+		cv::Mat_<float> weight = weights[layer];
+		float* m2 = (float*)weight.data;
+
+		cv::Mat_<float> resp_blas(weight.rows, resp.cols);
+		float* m3 = (float*)resp_blas.data;
+
+		// Perform matrix multiplication in OpenBLAS (fortran call)
+		float alpha1 = 1.0;
+		float beta1 = 0.0;
+		sgemm_("N", "N", &resp.cols, &weight.rows, &weight.cols, &alpha1, m1, &resp.cols, m2, &weight.cols, &beta1, m3, &resp.cols);
+
+		// The above is a faster version of this, by calling the fortran version directly
+		//cblas_sgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, resp.cols, weight.rows, weight.cols, 1, m1, resp.cols, m2, weight.cols, 0.0, m3, resp.cols);
+
+		// Adding the bias (bit ugly, but the fastest way to do this)
+		response = resp_blas;
+
+		float* data = (float*)response.data;
+		size_t height = response.rows;
+		size_t width = response.cols;
+		float* data_b = (float*)biases[layer].data;
+		for (size_t y = 0; y < height; ++y)
+		{
+			float bias = data_b[y];
+			for (size_t x = 0; x < width; ++x)
+			{
+				float in = *data + bias;
+				*data++ = in;
+			}
+		}
+
+		// Perform activation and add bias at the same time	
+		if (activation_function[layer] == 0) // Sigmoid
+		{
+
+			size_t resp_size = response.rows * response.cols;
+
+			// Iterate over the data directly
+			float* data = (float*)response.data;
+
+			for (size_t counter = 0; counter < resp_size; ++counter)
+			{
+				float in = *data;
+				*data++ = 1.0 / (1.0 + exp(-(in)));
+			}
+
+		}
+		else if (activation_function[layer] == 2)// ReLU
+		{
+			cv::threshold(response, response, 0, 0, cv::THRESH_TOZERO);
+		}
+
+	}
+
+	response = response * mapMatrix;
+	response = response.t();
+	response = response.reshape(1, response_height);
+	response = response.t();
+	cv::flip(response, response, 1);
+}
\ No newline at end of file
diff --git a/lib/local/LandmarkDetector/src/Patch_experts.cpp b/lib/local/LandmarkDetector/src/Patch_experts.cpp
index 730c9f13..8d6a7cac 100644
--- a/lib/local/LandmarkDetector/src/Patch_experts.cpp
+++ b/lib/local/LandmarkDetector/src/Patch_experts.cpp
@@ -226,9 +226,15 @@ void Patch_experts::Response(vector<cv::Mat_<float> >& patch_expert_responses, c
 				// Get intensity response either from the SVR, CCNF, or CEN patch experts (prefer CEN as they are the most accurate so far)
 				if (!cen_expert_intensity.empty())
 				{
-
-					cen_expert_intensity[scale][view_id][i].ResponseSparse(area_of_interest, patch_expert_responses[i], interp_mat);
-
+					// For space and memory saving use a mirrored patch expert
+					if(!cen_expert_intensity[scale][view_id][i].biases.empty())
+					{
+						cen_expert_intensity[scale][view_id][i].ResponseSparse(area_of_interest, patch_expert_responses[i], interp_mat);
+					}
+					else
+					{
+						cen_expert_intensity[scale][mirror_views.at<int>(view_id)][mirror_inds.at<int>(i)].ResponseSparse_mirror(area_of_interest, patch_expert_responses[i], interp_mat);
+					}
 					// A slower, but slightly more accurate version
 					//cen_expert_intensity[scale][view_id][i].Response(area_of_interest, patch_expert_responses[i]);
 
@@ -546,6 +552,9 @@ void Patch_experts::Read_CEN_patch_experts(string expert_location, std::vector<c
 		}
 		int numberOfPoints = visibility[0].rows;
 		
+		LandmarkDetector::ReadMatBin(patchesFile, mirror_inds);
+		LandmarkDetector::ReadMatBin(patchesFile, mirror_views);
+
 		// read the patches themselves
 		for (size_t i = 0; i < patches.size(); i++)
 		{
diff --git a/matlab_runners/Feature Point Experiments/results/300VW_49.txt b/matlab_runners/Feature Point Experiments/results/300VW_49.txt
index e9d21731..53ccdb9b 100644
--- a/matlab_runners/Feature Point Experiments/results/300VW_49.txt	
+++ b/matlab_runners/Feature Point Experiments/results/300VW_49.txt	
@@ -8,4 +8,4 @@ PO-CR	2.95	3.08	3.68
 CFAN	3.17	3.72	4.94
 ------------------------------
 OpenFace CLNF	2.83	3.38	3.82
-OpenFace CE-CLM	2.37	2.66	3.14
+OpenFace CE-CLM	2.40	2.65	3.16
diff --git a/matlab_runners/Feature Point Experiments/results/300VW_66.txt b/matlab_runners/Feature Point Experiments/results/300VW_66.txt
index 2f67ba38..0a28acaf 100644
--- a/matlab_runners/Feature Point Experiments/results/300VW_66.txt	
+++ b/matlab_runners/Feature Point Experiments/results/300VW_66.txt	
@@ -6,4 +6,4 @@ ICCR	3.37	3.45	4.46
 CFAN	3.93	4.43	6.39
 ------------------------------
 OpenFace CLNF	3.78	4.23	4.78
-OpenFace CE-CLM	3.05	3.52	3.99
+OpenFace CE-CLM	3.15	3.54	4.12
diff --git a/matlab_runners/Feature Point Experiments/results/300VW_OpenFace.mat b/matlab_runners/Feature Point Experiments/results/300VW_OpenFace.mat
index 24a9e137..e47afbef 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300VW_OpenFace.mat and b/matlab_runners/Feature Point Experiments/results/300VW_OpenFace.mat differ
diff --git a/matlab_runners/Feature Point Experiments/results/300VWres_49_cat1.pdf b/matlab_runners/Feature Point Experiments/results/300VWres_49_cat1.pdf
index e27300e6..5a8872d3 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300VWres_49_cat1.pdf and b/matlab_runners/Feature Point Experiments/results/300VWres_49_cat1.pdf differ
diff --git a/matlab_runners/Feature Point Experiments/results/300VWres_49_cat2.pdf b/matlab_runners/Feature Point Experiments/results/300VWres_49_cat2.pdf
index fc9c9673..74a3c485 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300VWres_49_cat2.pdf and b/matlab_runners/Feature Point Experiments/results/300VWres_49_cat2.pdf differ
diff --git a/matlab_runners/Feature Point Experiments/results/300VWres_49_cat3.pdf b/matlab_runners/Feature Point Experiments/results/300VWres_49_cat3.pdf
index 2e0d8edb..f0777e2c 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300VWres_49_cat3.pdf and b/matlab_runners/Feature Point Experiments/results/300VWres_49_cat3.pdf differ
diff --git a/matlab_runners/Feature Point Experiments/results/300VWres_66_cat1.pdf b/matlab_runners/Feature Point Experiments/results/300VWres_66_cat1.pdf
index 904d5924..0410fc15 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300VWres_66_cat1.pdf and b/matlab_runners/Feature Point Experiments/results/300VWres_66_cat1.pdf differ
diff --git a/matlab_runners/Feature Point Experiments/results/300VWres_66_cat2.pdf b/matlab_runners/Feature Point Experiments/results/300VWres_66_cat2.pdf
index 68c2038e..404b5be7 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300VWres_66_cat2.pdf and b/matlab_runners/Feature Point Experiments/results/300VWres_66_cat2.pdf differ
diff --git a/matlab_runners/Feature Point Experiments/results/300VWres_66_cat3.pdf b/matlab_runners/Feature Point Experiments/results/300VWres_66_cat3.pdf
index 9f04e942..505b4396 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300VWres_66_cat3.pdf and b/matlab_runners/Feature Point Experiments/results/300VWres_66_cat3.pdf differ
diff --git a/matlab_runners/Feature Point Experiments/results/300W_res.pdf b/matlab_runners/Feature Point Experiments/results/300W_res.pdf
index 52ac05e4..b6d3139f 100644
Binary files a/matlab_runners/Feature Point Experiments/results/300W_res.pdf and b/matlab_runners/Feature Point Experiments/results/300W_res.pdf differ
diff --git a/matlab_runners/Feature Point Experiments/results/fps_yt.mat b/matlab_runners/Feature Point Experiments/results/fps_yt.mat
index acf83d1f..212a0c1f 100644
Binary files a/matlab_runners/Feature Point Experiments/results/fps_yt.mat and b/matlab_runners/Feature Point Experiments/results/fps_yt.mat differ
diff --git a/matlab_runners/Feature Point Experiments/results/fps_yt.txt b/matlab_runners/Feature Point Experiments/results/fps_yt.txt
index cc0853fc..be792ccb 100644
--- a/matlab_runners/Feature Point Experiments/results/fps_yt.txt	
+++ b/matlab_runners/Feature Point Experiments/results/fps_yt.txt	
@@ -1,4 +1,4 @@
 Model, mean,  median
-OpenFace (CE-CLM):  0.0482,   0.0441
+OpenFace (CE-CLM):  0.0495,   0.0471
 OpenFace (CLNF):  0.0552,   0.0515
 CLM:   0.0647,   0.0593
diff --git a/matlab_runners/Feature Point Experiments/results/landmark_detections.mat b/matlab_runners/Feature Point Experiments/results/landmark_detections.mat
index f4fd91b5..6297e64c 100644
Binary files a/matlab_runners/Feature Point Experiments/results/landmark_detections.mat and b/matlab_runners/Feature Point Experiments/results/landmark_detections.mat differ
diff --git a/matlab_runners/Feature Point Experiments/results/landmark_detections.txt b/matlab_runners/Feature Point Experiments/results/landmark_detections.txt
index fb040d34..9a97b054 100644
--- a/matlab_runners/Feature Point Experiments/results/landmark_detections.txt	
+++ b/matlab_runners/Feature Point Experiments/results/landmark_detections.txt	
@@ -1,4 +1,4 @@
 Type, mean, median
-err ce-clm: 0.043160, 0.035038
+err ce-clm: 0.042293, 0.034776
 err clnf: 0.054968, 0.038257
 err svr: 0.070022, 0.049925
diff --git a/matlab_runners/Full_test_suite.m b/matlab_runners/Full_test_suite.m
index 28b07bc5..44ff1abb 100644
--- a/matlab_runners/Full_test_suite.m
+++ b/matlab_runners/Full_test_suite.m
@@ -5,9 +5,9 @@ tic
 %% Head pose
 cd('Head Pose Experiments');
 run_head_pose_tests_OpenFace_CECLM;
-assert(median(all_errors_biwi_OF(:)) < 3.0);
-assert(median(all_errors_bu_OF(:)) < 1.9);
-assert(median(all_errors_ict_OF(:)) < 2.1);
+assert(median(all_errors_biwi_OF(:)) < 2.7);
+assert(median(all_errors_bu_OF(:)) < 1.85);
+assert(median(all_errors_ict_OF(:)) < 1.9);
 run_head_pose_tests_OpenFace;
 assert(median(all_errors_biwi_OF(:)) < 2.7);
 assert(median(all_errors_bu_OF(:)) < 2.0);
@@ -17,10 +17,10 @@ cd('../');
 %% Features
 cd('Feature Point Experiments');
 run_OpenFace_feature_point_tests_300W;
-assert(median(err_ceclm) < 0.036);
+assert(median(err_ceclm) < 0.035);
 assert(median(err_clnf) < 0.039);
 run_yt_dataset;
-assert(median(ceclm_error) < 0.045);
+assert(median(ceclm_error) < 0.048);
 assert(median(clnf_error) < 0.053);
 run_300VW_dataset_OpenFace;
 assert(median(ceclm_error_49_cat_1) < 0.025);
diff --git a/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.mat b/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.mat
index b09eaf7a..b170b057 100644
Binary files a/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.mat and b/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.mat differ
diff --git a/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.txt b/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.txt
index 42008de9..a6fbaf3e 100644
--- a/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.txt	
+++ b/matlab_runners/Head Pose Experiments/results/Pose_OF_CECLM.txt	
@@ -1,4 +1,4 @@
 Dataset and model,        pitch,  yaw,  roll,  mean,  median
-biwi error:  7.111,   6.800, 3.853,  5.921,  2.915
-bu error:    2.774,   3.282, 2.368,  2.808,  1.873
-ict error:   3.983,   4.063, 3.096,  3.714,  2.040
+biwi error:  6.863,   6.167, 3.360,  5.464,  2.617
+bu error:    2.394,   3.245, 2.407,  2.682,  1.827
+ict error:   3.132,   3.482, 3.121,  3.245,  1.858
diff --git a/matlab_version/AU_training/data extraction/extract_features_Bosphorus.m b/matlab_version/AU_training/data extraction/extract_features_Bosphorus.m
index 5be45cbb..5974a0e0 100644
--- a/matlab_version/AU_training/data extraction/extract_features_Bosphorus.m	
+++ b/matlab_version/AU_training/data extraction/extract_features_Bosphorus.m	
@@ -18,10 +18,10 @@ for f1=1:numel(bosph_dirs)
         command = features_exe;
         input_file = [bosph_loc, '/' name '/', curr_vids(i).name];
         [~, curr_name, ~] = fileparts(curr_vids(i).name);
-        output_file = [out_loc, '/hog_aligned_rigid/',  curr_name, '/'];
+        output_file = [out_loc, '/hog_aligned_rigid_b/',  curr_name, '/'];
 
-        output_hog = [out_loc, '/hog_aligned_rigid/',curr_name '.hog'];
-        output_params = [out_loc, '/model_params/', curr_name '.txt'];
+        output_hog = [out_loc, '/hog_aligned_rigid_b/',curr_name '.hog'];
+        output_params = [out_loc, '/model_params_b/', curr_name '.txt'];
 
         command = cat(2, command, [' -rigid -f "' input_file '" -simalign "' output_file  '" -simscale 0.7 -simsize 112 ']);
         command = cat(2, command, [' -hogalign "' output_hog '"' ' -of "' output_params ]);
diff --git a/matlab_version/AU_training/data extraction/find_Bosphorus.m b/matlab_version/AU_training/data extraction/find_Bosphorus.m
index 39c101e5..ab65f89b 100644
--- a/matlab_version/AU_training/data extraction/find_Bosphorus.m	
+++ b/matlab_version/AU_training/data extraction/find_Bosphorus.m	
@@ -1,10 +1,12 @@
 if(exist('D:/Datasets/Bosphorus/', 'file'))
     Bosphorus_dir = 'D:\Datasets\Bosphorus/';   
+elseif(exist('E:/Datasets/Bosphorus/', 'file'))
+    Bosphorus_dir = 'E:\Datasets\Bosphorus/';   
 else
     fprintf('Bosphorus dataset location not found (or not defined)\n'); 
 end
 
-hog_data_dir = ['D:\Datasets\face_datasets'];
+hog_data_dir = ['E:\Datasets\face_datasets'];
 
 all_recs = dir([Bosphorus_dir, '/BosphorusDB/BosphorusDB/bs*']);
 all_recs_mat = cat(1, all_recs.name);
diff --git a/matlab_version/AU_training/instructions.txt b/matlab_version/AU_training/instructions.txt
index 91b41c7f..8b45e7f1 100644
--- a/matlab_version/AU_training/instructions.txt
+++ b/matlab_version/AU_training/instructions.txt
@@ -14,7 +14,7 @@ The instructions are for a Windows machine, but it should be fairly easy to adap
     - this will take some time: 4-20 hours depending on your machine as you will need to process a lot of visual data
     - you need to make sure you have the right datasets and they are in the right locations
 3. Create a PCA model
-    - run the createSubFaceModels.m in the pca_generation folder
+    - run the create_face_pca_models.m in the pca_generation folder
     - this will create a full, lower and upper face PCAs
     - this will take some time (30-60 minutes or so) and require lots of RAM (up to 16GB)
 4. Training the actual models, go to experiments/full_model_training
diff --git a/matlab_version/AU_training/pca_generation/createGenericFaceModel.m b/matlab_version/AU_training/pca_generation/createGenericFaceModel.m
deleted file mode 100644
index 8cef0ad4..00000000
--- a/matlab_version/AU_training/pca_generation/createGenericFaceModel.m
+++ /dev/null
@@ -1,67 +0,0 @@
-clear;
-
-%% CK+, FERA2011 UNBC, and Bosphorus datasets
-hog_dir = 'E:\Datasets/face_datasets/hog_aligned_rigid/';
-hog_files = dir([hog_dir, '*.hog']);
-
-[appearance_data, valid_inds, vid_ids_train] = Read_HOG_files_small(hog_files, hog_dir);
-appearance_data = appearance_data(valid_inds,:);
-vid_ids_train = vid_ids_train(valid_inds,:);
-
-%% DISFA
-hog_dir = 'E:\Datasets\DISFA\hog_aligned_rigid/';
-hog_files = dir([hog_dir, '*.hog']);
-
-[appearance_data_disfa, valid_inds, vid_ids_train_disfa] = Read_HOG_files_small(hog_files, hog_dir, 100);
-
-appearance_data_disfa = appearance_data_disfa(valid_inds,:);
-vid_ids_train_disfa = vid_ids_train_disfa(valid_inds,:);
-
-appearance_data = cat(1,appearance_data, appearance_data_disfa);
-vid_ids_train = cat(1,vid_ids_train, vid_ids_train_disfa);
-
-%% BP4D
-hog_dir = 'E:\Datasets\FERA_2015\bp4d\processed_data/train/';
-hog_files = dir([hog_dir, '*.hog']);
-
-[appearance_data_bp, valid_inds, vid_ids_train_bp] = Read_HOG_files_small(hog_files, hog_dir, 50);
-
-appearance_data_bp = appearance_data_bp(valid_inds,:);
-vid_ids_train_bp = vid_ids_train_bp(valid_inds,:);
-
-appearance_data = cat(1,appearance_data, appearance_data_bp);
-vid_ids_train = cat(1,vid_ids_train, vid_ids_train_bp);
-
-%% SEMAINE
-hog_dir = 'E:\Datasets\FERA_2015\semaine\processed_data\train\';
-hog_files = dir([hog_dir, '*.hog']);
-
-[appearance_data_semaine, valid_inds, vid_ids_train_semaine] = Read_HOG_files_small(hog_files, hog_dir, 300);
-
-appearance_data_semaine = appearance_data_semaine(valid_inds,:);
-vid_ids_train_semaine = vid_ids_train_semaine(valid_inds,:);
-
-appearance_data = cat(1,appearance_data, appearance_data_semaine);
-vid_ids_train = cat(1,vid_ids_train, vid_ids_train_semaine);
-
-%%
-means_norm = mean(appearance_data);
-stds_norm = std(appearance_data);
-
-normed_data = bsxfun(@times, bsxfun(@plus, appearance_data, -means_norm), 1./stds_norm);
-
-[PC, score, eigen_vals] = princomp(normed_data, 'econ');
-
-% Keep 95 percent of variability
-total_sum = sum(eigen_vals);
-count = numel(eigen_vals);
-for i=1:numel(eigen_vals)
-   if ((sum(eigen_vals(1:i)) / total_sum) >= 0.95)
-      count = i;
-      break;
-   end
-end
-
-PC = PC(:,1:count);
-
-save('generic_face_rigid.mat', 'PC', 'means_norm', 'stds_norm');    
diff --git a/matlab_version/AU_training/pca_generation/createSubFaceModels.m b/matlab_version/AU_training/pca_generation/create_face_pca_models.m
similarity index 91%
rename from matlab_version/AU_training/pca_generation/createSubFaceModels.m
rename to matlab_version/AU_training/pca_generation/create_face_pca_models.m
index 38f0f300..22b38bae 100644
--- a/matlab_version/AU_training/pca_generation/createSubFaceModels.m
+++ b/matlab_version/AU_training/pca_generation/create_face_pca_models.m
@@ -1,7 +1,7 @@
 clear;
 
 %% CK+, FERA2011, and UNBC datasets
-hog_dir = 'D:\Datasets/face_datasets/hog_aligned_rigid/';
+hog_dir = 'E:\Datasets/face_datasets/hog_aligned_rigid/';
 hog_files = dir([hog_dir, '*.hog']);
 
 [appearance_data, valid_inds, vid_ids_train] = Read_HOG_files_small(hog_files, hog_dir);
@@ -9,7 +9,7 @@ appearance_data = appearance_data(valid_inds,:);
 vid_ids_train = vid_ids_train(valid_inds,:);
 
 %% Bosphorus dataset
-hog_dir = 'D:\Datasets/face_datasets/hog_aligned_rigid_b/';
+hog_dir = 'E:\Datasets/face_datasets/hog_aligned_rigid_b/';
 hog_files = dir([hog_dir, '*.hog']);
 
 [appearance_data_bosph, valid_inds, vid_ids_train_bosph] = Read_HOG_files_small(hog_files, hog_dir);
@@ -21,7 +21,7 @@ appearance_data = cat(1,appearance_data, appearance_data_bosph);
 vid_ids_train = cat(1,vid_ids_train, vid_ids_train_bosph);
 
 %% DISFA
-hog_dir = 'D:\Datasets\DISFA\hog_aligned_rigid/';
+hog_dir = 'E:\Datasets\DISFA\hog_aligned_rigid/';
 hog_files = dir([hog_dir, '*.hog']);
 
 [appearance_data_disfa, valid_inds, vid_ids_train_disfa] = Read_HOG_files_small(hog_files, hog_dir, 100);
@@ -33,7 +33,7 @@ appearance_data = cat(1,appearance_data, appearance_data_disfa);
 vid_ids_train = cat(1,vid_ids_train, vid_ids_train_disfa);
 
 %% BP4D
-hog_dir = 'D:\Datasets\FERA_2015\bp4d\processed_data/train/';
+hog_dir = 'E:\Datasets\FERA_2015\bp4d\processed_data/train/';
 hog_files = dir([hog_dir, '*.hog']);
 
 [appearance_data_bp, valid_inds, vid_ids_train_bp] = Read_HOG_files_small(hog_files, hog_dir, 50);
@@ -45,7 +45,7 @@ appearance_data = cat(1,appearance_data, appearance_data_bp);
 vid_ids_train = cat(1,vid_ids_train, vid_ids_train_bp);
 
 %% SEMAINE
-hog_dir = 'D:\Datasets\FERA_2015\semaine\processed_data\train\';
+hog_dir = 'E:\Datasets\FERA_2015\semaine\processed_data\train\';
 hog_files = dir([hog_dir, '*.hog']);
 
 [appearance_data_semaine, valid_inds, vid_ids_train_semaine] = Read_HOG_files_small(hog_files, hog_dir, 300);
diff --git a/matlab_version/learn_error_mapping/early_term_cen_of.txt b/matlab_version/learn_error_mapping/early_term_cen_of.txt
new file mode 100644
index 00000000..f7c39aca
--- /dev/null
+++ b/matlab_version/learn_error_mapping/early_term_cen_of.txt
@@ -0,0 +1 @@
+1.000 0.969 1.017 0.954 0.954 1.017 0.969 0.000 0.047 0.164 -0.375 -0.375 0.164 0.047 -0.270 -0.260 -0.170 0.250 0.250 -0.170 -0.260 
\ No newline at end of file
diff --git a/matlab_version/learn_error_mapping/readme.txt b/matlab_version/learn_error_mapping/readme.txt
index 4c90acb4..fef49698 100644
--- a/matlab_version/learn_error_mapping/readme.txt
+++ b/matlab_version/learn_error_mapping/readme.txt
@@ -9,4 +9,4 @@ Then:
 learn_error_pred_general.m
 learn_error_pred_menpo.m
 
-To use in C++ also generate the early_term_cen.txt file using output_corrections.m
\ No newline at end of file
+To use in C++ also generate the early_term_cen_of.txt file using output_corrections.m, this is already generated so no need to re-run it.
\ No newline at end of file
diff --git a/matlab_version/models/cen/create_cen_experts_gen.m b/matlab_version/models/cen/create_cen_experts_gen.m
index 4c6028f8..c5106ab7 100644
--- a/matlab_version/models/cen/create_cen_experts_gen.m
+++ b/matlab_version/models/cen/create_cen_experts_gen.m
@@ -109,5 +109,5 @@ for s=scales
     end
     trainingScale = str2num(s{1});
     save(['cen_patches_', s{1} '_general.mat'], 'trainingScale', 'centers', 'visiIndex', 'patch_experts', 'normalisationOptions');
-%     write_patch_expert_bin(['cen_patches_', s{1} '_general.dat'], trainingScale, centers, visiIndex, patch_experts);
-end
\ No newline at end of file
+
+end
diff --git a/matlab_version/models/cen/crete_cen_experts_OF.m b/matlab_version/models/cen/crete_cen_experts_OF.m
new file mode 100644
index 00000000..653e432c
--- /dev/null
+++ b/matlab_version/models/cen/crete_cen_experts_OF.m
@@ -0,0 +1,58 @@
+clear;
+%%
+mirrorInds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
+32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
+61,65;62,64;68,66];   
+
+mirror_inds = [1:68];
+mirror_inds(mirrorInds(:,1)) = mirrorInds(:,2);
+mirror_inds(mirrorInds(:,2)) = mirrorInds(:,1);
+
+scales = {'0.25', '0.35', '0.50', '1.00'};
+
+for s=scales
+
+    gen_experts = load(sprintf('cen_patches_%s_general.mat', s{1}));
+    load(sprintf('cen_patches_%s_menpo.mat', s{1}));
+    
+    for c=1:size(visiIndex,1)
+        for i=1:size(visiIndex,2)
+            % If present in general and not menpo replace
+            if(gen_experts.visiIndex(c,i) && ~visiIndex(c,i))
+                visiIndex(c,i) = 1;
+                patch_experts.correlations(c,i) = gen_experts.patch_experts.correlations(c,i);
+                patch_experts.rms_errors(c,i) = gen_experts.patch_experts.rms_errors(c,i);
+                patch_experts.patch_experts(c,i) = gen_experts.patch_experts.patch_experts(c,i);
+            elseif(~visiIndex(c,i))
+                patch_experts.correlations(c,i) = 0;
+                patch_experts.rms_errors(c,i) = 0;
+                patch_experts.patch_experts(c,i) = {[]}; 
+            end
+                
+        end
+    end
+    % Work out the frontal view and remove mirror indices for it
+    [~, frontal] = min(mean(abs(bsxfun(@plus, centers, [0,0,0])')));
+
+    % First clean up the frontal view
+    patch_experts.patch_experts(frontal, mirrorInds(:,2)) = {[]};
+
+    % Work out which views have mirrors of each other, and keep only one set
+    % of them
+    n_views = size(visiIndex,1);
+
+    mirror_view = 1:n_views;
+
+    for i = 1:n_views
+        [~, mirror_view(i)] = min(mean(abs(bsxfun(@plus, centers, centers(i,:))')));
+    end
+
+    % Remove a set of mirror indices
+    to_rem = mirror_view < 1:n_views;
+
+    patch_experts.patch_experts(to_rem, :) = {[]};
+
+    trainingScale = str2num(s{1});
+    write_patch_expert_bin_simple(['cen_patches_', s{1} '_of.dat'], trainingScale, centers, visiIndex, patch_experts, mirror_inds - 1, mirror_view - 1);
+
+end
\ No newline at end of file
diff --git a/matlab_version/models/cen/readme.txt b/matlab_version/models/cen/readme.txt
index bea82d24..527fa940 100644
--- a/matlab_version/models/cen/readme.txt
+++ b/matlab_version/models/cen/readme.txt
@@ -1,4 +1,7 @@
 Scripts for creating CEN patch experts from already trained models.
 
 To create one from 300W + MultiPIE - create_cen_experts_gen.m
-To create one from 300W + MultiPIE + Menpo - create_cen_experts_menpo.m
\ No newline at end of file
+To create one from 300W + MultiPIE + Menpo - create_cen_experts_menpo.m
+
+To create one used in OpenFace for C++ code:
+create_cen_experts_OF.m (this uses both the general and menpo experts to create a joint one, with general ones used when menpo unavailable for that view)
\ No newline at end of file
diff --git a/matlab_version/models/cen/write_patch_expert_bin.m b/matlab_version/models/cen/write_patch_expert_bin.m
index 86f05577..44f2aaf9 100644
--- a/matlab_version/models/cen/write_patch_expert_bin.m
+++ b/matlab_version/models/cen/write_patch_expert_bin.m
@@ -25,7 +25,7 @@ function write_patch_expert_bin(location, trainingScale, centers, visiIndex, pat
     for i=1:n_views
         for j=1:n_landmarks
 
-            % Write out that we're writing a dpn patch expert of 11x11 support region
+            % Write out that we're writing a CEN patch expert of 11x11 support region
             fwrite(patches_file, 6, 'int');
             fwrite(patches_file, 11, 'int');
             fwrite(patches_file, 11, 'int');
diff --git a/matlab_version/models/cen/write_patch_expert_bin_simple.m b/matlab_version/models/cen/write_patch_expert_bin_simple.m
new file mode 100644
index 00000000..45c05766
--- /dev/null
+++ b/matlab_version/models/cen/write_patch_expert_bin_simple.m
@@ -0,0 +1,79 @@
+function write_patch_expert_bin_simple(location, trainingScale, centers, visiIndex, patch_experts, mirror_inds, mirror_view)
+          
+    patches_file = fopen(location, 'w');        
+    
+    [n_views, n_landmarks, ~] = size(patch_experts.correlations);
+
+    % write out the scaling factor as this is what will be used when
+    % fitting on the window
+    fwrite(patches_file, trainingScale, 'float64');
+    
+    fwrite(patches_file, n_views, 'int');
+    
+    % Write out the information about the view's and centers here
+    for i=1:n_views
+        % this indicates that we're writing a 3x1 double matrix
+        writeMatrixBin(patches_file, centers(i,:)', 6);
+    end
+            
+    % Write out the visibilities
+    for i=1:n_views
+        % this indicates that we're writing a 3x1 double matrix
+        writeMatrixBin(patches_file, visiIndex(i,:)', 4);
+    end
+            
+    % Write out the mirror indices
+    writeMatrixBin(patches_file, mirror_inds, 4);
+    
+    % Write out the mirror views
+    writeMatrixBin(patches_file, mirror_view, 4);
+    
+    for i=1:n_views
+        for j=1:n_landmarks
+
+            % Write out that we're writing a CEN patch expert of 11x11 support region
+            fwrite(patches_file, 6, 'int');
+            fwrite(patches_file, 11, 'int');
+            fwrite(patches_file, 11, 'int');
+            
+            if(~visiIndex(i,j))       
+                % Write out that there won't be any neurons for this
+                % landmark
+                fwrite(patches_file, 0, 'int');
+                fwrite(patches_file, patch_experts.correlations(i,j), 'float64'); % also writing out a fake correlation
+            else
+                num_layers = numel(patch_experts.patch_experts{i,j})/2;
+
+                fwrite(patches_file, num_layers, 'int');
+
+                for n=1:num_layers
+                    
+                    % output the actual layer
+                    
+                    % Layer type, bias, weights
+                    
+                    % Type of layer, first two are relu, the final one is a
+                    % sigmoid (0 - sigmoid, 1 - tanh_opt, 2 - ReLU)
+                    if(n < 3)
+                        fwrite(patches_file, 2, 'int');
+                    else
+                        fwrite(patches_file, 0, 'int');
+                    end
+                                                            
+                    bias = patch_experts.patch_experts{i,j}{n*2};
+                    
+                    weights = patch_experts.patch_experts{i,j}{n*2-1};
+
+                    % the actual bias/weight matrix
+                    writeMatrixBin(patches_file, bias, 5);
+                    writeMatrixBin(patches_file, weights, 5);
+                end
+                
+                % finally write out the confidence
+                fwrite(patches_file, patch_experts.correlations(i,j), 'float64');
+                
+            end    
+        end
+    end
+
+    fclose(patches_file);