diff --git a/matlab_version/bounding_box_mapping/back/learn_mapping_mtcnn_menpo_v2.m b/matlab_version/bounding_box_mapping/back/learn_mapping_mtcnn_menpo_v2.m
new file mode 100644
index 00000000..07554de5
--- /dev/null
+++ b/matlab_version/bounding_box_mapping/back/learn_mapping_mtcnn_menpo_v2.m
@@ -0,0 +1,172 @@
+clear;
+% Get image files
+load('menpo_train.mat');
+img_names = struct;
+for i=1:size(bboxes,2)
+    img_names(i).name = bboxes(i).name;
+end
+
+load('menpo_mtcnn.mat')
+
+frontal = true(size(bboxes,1), 1);
+right = false(size(bboxes,1), 1);
+
+data_dir = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+for i=1:size(bboxes,1)
+              
+    % For non-frontal need to flip the view to other dir?    
+    if(size(gt_labels{i},1) ==39)
+        frontal(i) = false;
+        landmarks = gt_labels{i};
+        % Determine the direction
+        sum_c = 0;
+        for k=1:11
+            step = (landmarks(k+1,1) - landmarks(k,1)) * (landmarks(k+1,2) + landmarks(k,2));
+            sum_c = sum_c + step;
+        end
+        right(i) = true;
+
+        % Flip the detection and ground truth if profile is different
+        % direction
+        if(sum_c < 0)
+            width = size(imread([data_dir, img_names(i).name]),2);
+            
+            if(dets(i,3) ~= 0)
+                tx = dets(i,1);
+                width_f = dets(i,3) - dets(i,1);
+
+                dets(i,1) = width - (tx+width_f);
+                dets(i,3) = width - tx;
+            end            
+            tx = bboxes(i,1);
+            width_f = bboxes(i,3) - bboxes(i,1);
+            
+            bboxes(i,1) = width - (tx+width_f);
+            bboxes(i,3) = width - tx;
+        end
+        
+    end
+
+end
+bboxes_det = dets;
+bboxes_gt = bboxes;
+
+non_detected = bboxes_det(:,3) == 0;
+
+% Removing the outliers
+widths_gt = bboxes_gt(:,3) - bboxes_gt(:,1);
+widths_det = bboxes_det(:,3) - bboxes_det(:,1);
+
+heights_gt = bboxes_gt(:,4) - bboxes_gt(:,2);
+heights_det = bboxes_det(:,4) - bboxes_det(:,2);
+
+tx_gt =  bboxes_gt(:,1);
+ty_gt =  bboxes_gt(:,2);
+
+tx_det = bboxes_det(:,1);
+ty_det = bboxes_det(:,2);
+
+bad_det_1 = abs(1 - widths_gt ./ widths_det) > 0.5;
+bad_det_2 = abs(1 - heights_gt ./ heights_det) > 0.5;
+
+bad_det_3 = abs((tx_gt - tx_det) ./ widths_det) > 0.4;
+bad_det_4 = abs((ty_gt - ty_det) ./ heights_det) > 0.5;
+
+non_detected = non_detected | bad_det_1 | bad_det_2 | bad_det_3 | bad_det_4;
+
+%% Perform correction for frontal images
+bboxes_gt = bboxes(frontal & (~non_detected),:);
+bboxes_det = dets(frontal & (~non_detected),:);
+
+% Find the width and height mappings
+widths_gt = bboxes_gt(:,3) - bboxes_gt(:,1);
+widths_det = bboxes_det(:,3) - bboxes_det(:,1);
+
+heights_gt = bboxes_gt(:,4) - bboxes_gt(:,2);
+heights_det = bboxes_det(:,4) - bboxes_det(:,2);
+
+s_width_f = widths_det \ widths_gt;
+s_height_f = heights_det \ heights_gt;
+
+tx_gt =  bboxes_gt(:,1);
+ty_gt =  bboxes_gt(:,2);
+
+tx_det = bboxes_det(:,1);
+ty_det = bboxes_det(:,2);
+
+s_tx_f = median((tx_gt - tx_det) ./ widths_det);
+s_ty_f = median((ty_gt - ty_det) ./ heights_det);
+
+%% Now do the profile views
+bboxes_gt = bboxes(right & (~non_detected),:);
+bboxes_det = dets(right & (~non_detected),:);
+
+% Find the width and height mappings
+widths_gt = bboxes_gt(:,3) - bboxes_gt(:,1);
+widths_det = bboxes_det(:,3) - bboxes_det(:,1);
+
+heights_gt = bboxes_gt(:,4) - bboxes_gt(:,2);
+heights_det = bboxes_det(:,4) - bboxes_det(:,2);
+
+s_width_p = widths_det \ widths_gt;
+s_height_p = heights_det \ heights_gt;
+
+tx_gt =  bboxes_gt(:,1);
+ty_gt =  bboxes_gt(:,2);
+
+tx_det = bboxes_det(:,1);
+ty_det = bboxes_det(:,2);
+
+s_tx_p = median((tx_gt - tx_det) ./ widths_det);
+s_ty_p = median((ty_gt - ty_det) ./ heights_det);
+
+%% Perform the frontal correction
+widths_det = dets(frontal & (~non_detected),3) - dets(frontal & (~non_detected),1);
+heights_det = dets(frontal & (~non_detected),4) - dets(frontal & (~non_detected),2);
+tx_det = dets(frontal & (~non_detected),1);
+ty_det = dets(frontal & (~non_detected),2);
+
+new_widths_f = widths_det * s_width_f;
+new_heights_f = heights_det * s_height_f;
+new_tx_f = widths_det * s_tx_f + tx_det;
+new_ty_f = heights_det * s_ty_f + ty_det;
+
+%% Perform the profile correction
+widths_det = dets(right & (~non_detected),3) - dets(right & (~non_detected),1);
+heights_det = dets(right & (~non_detected),4) - dets(right & (~non_detected),2);
+tx_det = dets(right & (~non_detected),1);
+ty_det = dets(right & (~non_detected),2);
+
+new_widths_p = widths_det * s_width_p;
+new_heights_p = heights_det * s_height_p;
+new_tx_p = widths_det * s_tx_p + tx_det;
+new_ty_p = heights_det * s_ty_p + ty_det;
+
+%% Perform the evaluation
+overlaps = zeros(numel(widths_det), 1);
+new_overlaps = zeros(numel(widths_det), 1);
+
+new_bboxes = zeros(size(bboxes));
+new_bboxes(frontal & (~non_detected),1) = new_tx_f;
+new_bboxes(frontal & (~non_detected),2) = new_ty_f;
+new_bboxes(frontal & (~non_detected),3) = new_tx_f + new_widths_f;
+new_bboxes(frontal & (~non_detected),4) = new_ty_f + new_heights_f;
+
+new_bboxes(right & (~non_detected),1) = new_tx_p;
+new_bboxes(right & (~non_detected),2) = new_ty_p;
+new_bboxes(right & (~non_detected),3) = new_tx_p + new_widths_p;
+new_bboxes(right & (~non_detected),4) = new_ty_p + new_heights_p;
+
+bboxes_gt = bboxes(~non_detected,:);
+bboxes_det = dets(~non_detected,:);
+new_bboxes = new_bboxes(~non_detected,:);
+
+for i=1:size(bboxes_gt,1)
+    bbox_gt = bboxes_gt(i,:);
+    bbox_old = bboxes_det(i,:);
+    overlaps(i) = overlap(bbox_gt, bbox_old);
+    bbox_new = new_bboxes(i,:);
+    new_overlaps(i) = overlap(bbox_gt, bbox_new);
+end
+
+fprintf('Orig - %.3f, now - %.3f\n', mean(overlaps), mean(new_overlaps));
\ No newline at end of file
diff --git a/matlab_version/bounding_box_mapping/back/learn_mapping_mtcnn_menpo_v3.m b/matlab_version/bounding_box_mapping/back/learn_mapping_mtcnn_menpo_v3.m
new file mode 100644
index 00000000..a17f9d69
--- /dev/null
+++ b/matlab_version/bounding_box_mapping/back/learn_mapping_mtcnn_menpo_v3.m
@@ -0,0 +1,350 @@
+clear;
+
+%% Some useful variables for moving the landmarks to common frame and for mirroring
+left_to_frontal_map = [17,28; 18,29; 19,30; 20,31;
+                       21,34; 22,32; 23,39; 24,38; 25,37; 26,42; 27,41;
+                       28,52; 29,51; 30,50; 31,49; 32,60; 33,59; 34,58;
+                       35,63; 36,62; 37,61; 38,68; 39,67];
+                   
+right_to_frontal_map = [17,28; 18,29; 19,30; 20,31;
+                       21,34; 22,36; 23,44; 24,45; 25,46; 26,47; 27,48;
+                       28,52; 29,53; 30,54; 31,55; 32,56; 33,57; 34,58;
+                       35,63; 36,64; 37,65; 38,66; 39,67];
+
+mirror_inds = [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];
+          
+%%
+
+% Correct the bounding box location in such a way that actual
+% initialization of the landmarks has the smallest distance to GT
+
+% the default PDM to use
+pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
+load(pdmLoc);
+% Set up frontal and profile mean shapes
+num_points = numel(M)/3;
+m_frontal = reshape(M, num_points, 3)';
+width_model_f = max(m_frontal(1,:)) - min(m_frontal(1,:));
+height_model_f = max(m_frontal(2,:)) - min(m_frontal(2,:));
+
+rot = Euler2Rot([0,-70*pi/180,0]); 
+m_profile = rot * reshape(M, num_points, 3)';
+width_model_p = max(m_profile(1,:)) - min(m_profile(1,:));
+height_model_p = max(m_profile(2,:)) - min(m_profile(2,:));
+
+% Get image files
+load('menpo_train.mat');
+img_names = struct;
+for i=1:size(bboxes,2)
+    img_names(i).name = bboxes(i).name;
+end
+
+load('menpo_mtcnn.mat')
+
+inits_frontal = [];
+gt_frontal = [];
+widths_frontal = [];
+heights_frontal = [];
+scales_frontal = [];
+txs_frontal = [];
+tys_frontal = [];
+
+inits_p = [];
+gt_p = [];
+widths_p = [];
+heights_p = [];
+scales_p = [];
+txs_p = [];
+tys_p = [];
+
+data_dir = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+for i=1:size(bboxes,1)
+              
+    % Check if the detection is correct first
+    tx_gt =  bboxes(i,1);
+    ty_gt =  bboxes(i,2);
+
+    tx_det = dets(i,1);
+    ty_det = dets(i,2);
+
+    widths_gt = bboxes(i,3) - bboxes(i,1);
+    widths_det = dets(i,3) - dets(i,1);
+
+    heights_gt = bboxes(i,4) - bboxes(i,2);
+    heights_det = dets(i,4) - dets(i,2);
+
+    bad_det_1 = abs(1 - widths_gt ./ widths_det) > 0.5;
+    bad_det_2 = abs(1 - heights_gt ./ heights_det) > 0.5;
+
+    bad_det_3 = abs((tx_gt - tx_det) ./ widths_det) > 0.4;
+    bad_det_4 = abs((ty_gt - ty_det) ./ heights_det) > 0.5;
+
+    width = bboxes(i,3) - bboxes(i,1);
+    if(width == 0 || bad_det_1 || bad_det_2 || bad_det_3 || bad_det_4)
+        continue;
+    end
+    
+    landmarks = gt_labels{i};
+    bounding_box = dets(i,:); 
+
+    width = bounding_box(3) - bounding_box(1);
+    height = bounding_box(4) - bounding_box(2);
+
+    % For non-frontal need to flip the view to other dir?    
+    if(size(landmarks,1) ==39)
+        
+        landmark_labels = zeros(68,2); 
+        
+        % Determine the direction
+        sum_c = 0;
+        for k=1:11
+            step = (landmarks(k+1,1) - landmarks(k,1)) * (landmarks(k+1,2) + landmarks(k,2));
+            sum_c = sum_c + step;
+        end
+
+        % Flip the detection and ground truth if profile is different
+        % direction
+        if(sum_c < 0)
+            width_img = size(imread([data_dir, img_names(i).name]),2);
+            
+            if(dets(i,3) ~= 0)
+                tx = dets(i,1);
+                width_f = dets(i,3) - dets(i,1);
+
+                dets(i,1) = width_img - (tx+width_f);
+                dets(i,3) = width_img - tx;
+            end            
+            tx = bboxes(i,1);
+            width_f = bboxes(i,3) - bboxes(i,1);
+            
+            bboxes(i,1) = width_img - (tx+width_f);
+            bboxes(i,3) = width_img - tx;
+            
+            outline = iterate_piece_wise(landmarks(10:-1:1,:), 9);
+            brow = iterate_piece_wise(landmarks(16:-1:13,:), 5);
+
+            landmark_labels(9:17,:) = outline;
+            landmark_labels(23:27,:) = brow;
+
+            landmark_labels(right_to_frontal_map(:,2),:) = landmarks(right_to_frontal_map(:,1),:);    
+            
+            % Flip landmarks left to right
+            mirror_lbls = landmark_labels;
+            mirror_lbls(mirror_lbls ==0) = nan;
+            mirror_lbls(:,1) = width -mirror_lbls(:,1);
+            tmp1 = mirror_lbls(mirror_inds(:,1),:);
+            tmp2 = mirror_lbls(mirror_inds(:,2),:);            
+            mirror_lbls(mirror_inds(:,2),:) = tmp1;
+            mirror_lbls(mirror_inds(:,1),:) = tmp2;      
+            landmark_labels = mirror_lbls;
+            landmark_labels(isnan(landmark_labels)) = 0;
+            
+        else
+            outline = iterate_piece_wise(landmarks(1:10,:), 9);
+            brow = iterate_piece_wise(landmarks(13:16,:), 5);
+            landmark_labels(1:9,:) = outline;
+            landmark_labels(18:22,:) = brow;
+            landmark_labels(left_to_frontal_map(:,2),:) = landmarks(left_to_frontal_map(:,1),:);
+        end
+                             
+        widths_p = cat(1, widths_p, width);
+        heights_p = cat(1, heights_p, height);
+
+        a = ((0.96 * width / width_model_p) + (0.7901 *height/ height_model_p)) / 2;
+%         a = ((width / width_model_p) + (height/ height_model_p)) / 2;        
+        
+        scales_p = cat(1, scales_p, a);
+
+        tx = (bounding_box(3) + bounding_box(1))/2;
+        ty = (bounding_box(4) + bounding_box(2))/2;
+
+        % correct it so that the bounding box is just around the minimum
+        % and maximum point in the initialised face
+        tx = tx - a*(min(m_profile(1,:)) + max(m_profile(1,:)))/2;
+        ty = ty - a*(min(m_profile(2,:)) + max(m_profile(2,:)))/2;
+        
+        tx = tx - 0.1632 * width;
+        ty = ty + 0.0944 * height;
+        
+        txs_p = cat(1, txs_p, tx);
+        tys_p = cat(1, tys_p, ty);
+        
+        % visualisation of the initial state
+        global_params = [a, 0, -70*pi/180, 0, tx, ty]';
+        local_params = zeros(numel(E), 1);    
+        % shape around which the patch experts will be evaluated in the original image
+        [shape2D] = GetShapeOrtho(M, V, local_params, global_params);
+        
+        valid_points = landmark_labels(:,1) ~= 0;
+        
+        shape2D_img = shape2D(valid_points,1:2);
+        inits_p = cat(3, inits_p, shape2D_img);
+        gt_p = cat(3, gt_p, landmark_labels(valid_points,:));
+%         if(sum_c>0)
+%             hold off;imshow(imread([data_dir, img_names(i).name]));hold on;plot(shape2D_img(:,1), shape2D_img(:,2), '.r');hold on;rectangle('Position', [bounding_box(1), bounding_box(2), bounding_box(3)-bounding_box(1), bounding_box(4)-bounding_box(2)]);
+%         else
+%             hold off;imshow(fliplr(imread([data_dir, img_names(i).name])));hold on;plot(shape2D_img(:,1), shape2D_img(:,2), '.r');hold on;rectangle('Position', [bounding_box(1), bounding_box(2), bounding_box(3)-bounding_box(1), bounding_box(4)-bounding_box(2)]);            
+%         end
+    else
+        widths_frontal = cat(1, widths_frontal, width);
+        heights_frontal = cat(1, heights_frontal, height);
+
+        a = ((0.8283 * width / width_model_f) + (0.8454 *height/ height_model_f)) / 2;
+%         a = ((width / width_model_f) + (height/ height_model_f)) / 2;        
+        
+        scales_frontal = cat(1, scales_frontal, a);
+
+        tx = (bounding_box(3) + bounding_box(1))/2;
+        ty = (bounding_box(4) + bounding_box(2))/2;
+
+        % correct it so that the bounding box is just around the minimum
+        % and maximum point in the initialised face
+        tx = tx - a*(min(m_frontal(1,:)) + max(m_frontal(1,:)))/2;
+        ty = ty - a*(min(m_frontal(2,:)) + max(m_frontal(2,:)))/2;
+
+        tx = tx - 0.00387 * width;
+        ty = ty + 0.1469 * height;
+%         
+        txs_frontal = cat(1, txs_frontal, tx);
+        tys_frontal = cat(1, tys_frontal, ty);
+        
+        % visualisation of the initial state
+        global_params = [a, 0, 0, 0, tx, ty]';
+        local_params = zeros(numel(E), 1);    
+        % shape around which the patch experts will be evaluated in the original image
+        [shape2D] = GetShapeOrtho(M, V, local_params, global_params);
+        shape2D_img = shape2D(:,1:2);
+        inits_frontal = cat(3, inits_frontal, shape2D_img);
+        gt_frontal = cat(3, gt_frontal, landmarks);
+%         hold off;imshow(imread([data_dir, img_names(i).name]));hold on;plot(shape2D_img(:,1), shape2D_img(:,2), '.r');hold on;rectangle('Position', [bounding_box(1), bounding_box(2), bounding_box(3)-bounding_box(1), bounding_box(4)-bounding_box(2)]);
+
+    end
+
+end
+
+%% Determine parameters for frontal
+widths_frontal = repmat(widths_frontal, 1, 68)';
+widths_frontal = widths_frontal(:);
+heights_frontal = repmat(heights_frontal, 1, 68)';
+heights_frontal = heights_frontal(:);
+scales_frontal = repmat(scales_frontal, 1, 68)';
+scales_frontal = scales_frontal(:);
+txs_frontal = repmat(txs_frontal, 1, 68)';
+txs_frontal = txs_frontal(:);
+tys_frontal = repmat(tys_frontal, 1, 68)';
+tys_frontal = tys_frontal(:);
+
+xs_gt = squeeze(gt_frontal(:,1,:));
+xs_gt = xs_gt(:);
+xs_init = squeeze(inits_frontal(:,1,:));
+xs_init = xs_init(:);
+
+m_frontal_x = m_frontal(1,:);
+m_frontal_x = repmat(m_frontal_x, size(gt_frontal,3),1)';
+m_frontal_x = m_frontal_x(:);
+
+to_pred = (xs_gt - txs_frontal)./widths_frontal;
+
+feats = [scales_frontal .* m_frontal_x./widths_frontal, widths_frontal./widths_frontal];
+frontal_w = feats \ to_pred;
+s_width_f = frontal_w(1);
+s_tx_f = frontal_w(2);
+
+ys_gt = squeeze(gt_frontal(:,2,:));
+ys_gt = ys_gt(:);
+ys_init = squeeze(inits_frontal(:,2,:));
+ys_init = ys_init(:);
+
+m_frontal_y = m_frontal(2,:);
+m_frontal_y = repmat(m_frontal_y, size(gt_frontal,3),1)';
+m_frontal_y = m_frontal_y(:);
+
+to_pred = (ys_gt - tys_frontal)./heights_frontal;
+
+feats = [scales_frontal .* m_frontal_y./heights_frontal, heights_frontal./heights_frontal];
+frontal_w = feats \ to_pred;
+s_height_f= frontal_w(1);
+s_ty_f = frontal_w(2);
+
+fprintf('Frontal err x - %.3f, err y - %.3f\n', mean(abs(xs_gt-xs_init)./widths_frontal), mean(abs(ys_gt-ys_init)./heights_frontal));
+fprintf('Frontal offs x - %.3f, offs y - %.3f\n', mean((xs_gt-xs_init)./widths_frontal), mean((ys_gt-ys_init)./heights_frontal));
+
+%%
+widths_p = repmat(widths_p, 1, 37)';
+widths_p = widths_p(:);
+heights_p = repmat(heights_p, 1, 37)';
+heights_p = heights_p(:);
+scales_p = repmat(scales_p, 1, 37)';
+scales_p = scales_p(:);
+txs_p = repmat(txs_p, 1, 37)';
+txs_p = txs_p(:);
+tys_p = repmat(tys_p, 1, 37)';
+tys_p = tys_p(:);
+
+% Perform correction due to profile
+tx_off = (min(m_profile(1,valid_points)) + max(m_profile(1,valid_points)))/2;
+txs_p = txs_p + tx_off .* scales_p;
+
+ty_off = (min(m_profile(2,valid_points)) + max(m_profile(2,valid_points)))/2;
+tys_p = tys_p + ty_off .* scales_p;
+
+xs_gt = squeeze(gt_p(:,1,:));
+xs_gt = xs_gt(:);
+xs_init = squeeze(inits_p(:,1,:));
+xs_init = xs_init(:);
+
+m_p_x = m_profile(1,valid_points);
+m_p_x = repmat(m_p_x, size(gt_p,3),1)';
+m_p_x = m_p_x(:);
+
+to_pred = (xs_gt - txs_p)./widths_p;
+
+feats = [scales_p .* m_p_x./widths_p, widths_p./widths_p];
+p_w = feats \ to_pred;
+s_width_p = p_w(1);
+s_tx_p = p_w(2);
+
+ys_gt = squeeze(gt_p(:,2,:));
+ys_gt = ys_gt(:);
+ys_init = squeeze(inits_p(:,2,:));
+ys_init = ys_init(:);
+
+m_p_y = m_profile(2,valid_points);
+m_p_y = repmat(m_p_y, size(gt_p,3),1)';
+m_p_y = m_p_y(:);
+
+to_pred = (ys_gt - tys_p)./heights_p;
+
+feats = [scales_p .* m_p_y./heights_p, heights_p./heights_p];
+p_w = feats \ to_pred;
+s_height_p = p_w(1);
+s_ty_p = p_w(2);
+
+fprintf('Profile err x - %.3f, err y - %.3f\n', mean(abs(xs_gt-xs_init)./widths_p), mean(abs(ys_gt-ys_init)./heights_p));
+fprintf('Profile offs x - %.3f, offs y - %.3f\n', mean((xs_gt-xs_init)./widths_p), mean((ys_gt-ys_init)./heights_p));
+
+% Without any corrections
+% Frontal err x - 0.116, err y - 0.150
+% Frontal offs x - 0.039, offs y - 0.147
+% Profile err x - 0.414, err y - 0.148
+% Profile offs x - -0.332, offs y - 0.143
+
+% % With original v1 corrections
+% Frontal err x - 0.110, err y - 0.107
+% Frontal offs x - 0.047, offs y - -0.104
+% Profile err x - 0.413, err y - 0.111
+% Profile offs x - -0.313, offs y - -0.106
+
+% With more specific v2 corrections
+% Frontal err x - 0.109, err y - 0.105
+% Frontal offs x - 0.044, offs y - -0.102
+% Profile err x - 0.417, err y - 0.116
+% Profile offs x - -0.259, offs y - -0.112
+
+% With V3 corrections
+% Frontal err x - 0.109, err y - 0.044
+% Frontal offs x - 0.044, offs y - -0.007
+% Profile err x - 0.451, err y - 0.065
+% Profile offs x - -0.155, offs y - 0.044
\ No newline at end of file
diff --git a/matlab_version/bounding_box_mapping/collect_transform_mtcnn_menpo.m b/matlab_version/bounding_box_mapping/collect_transform_mtcnn_menpo.m
new file mode 100644
index 00000000..028c3aa0
--- /dev/null
+++ b/matlab_version/bounding_box_mapping/collect_transform_mtcnn_menpo.m
@@ -0,0 +1,28 @@
+load('menpo_train.mat');
+dataset_loc = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+bboxes_st = bboxes;
+
+% The offsets and detector bounding boxes
+dets = zeros(numel(bboxes_st), 4);
+bboxes = zeros(numel(bboxes_st), 4);
+gt_labels = cell(numel(bboxes_st),1);
+%%
+for i=1:numel(bboxes_st)
+
+    name = bboxes_st(i).name(1:end-4);    
+    landmarks = importdata([dataset_loc, name, '.pts'], ' ', 3);
+    landmarks = landmarks.data;
+    
+    bbox_gt = [min(landmarks(:,1)), min(landmarks(:,2)), max(landmarks(:,1)), max(landmarks(:,2))];
+    bboxes(i,:) = bbox_gt;
+    gt_labels{i} = landmarks;
+    % zone in on a smaller version of the image
+    if(~isempty(bboxes_st(i).bbox))
+        dets(i,:) = [bboxes_st(i).bbox(1), bboxes_st(i).bbox(2), bboxes_st(i).bbox(3), bboxes_st(i).bbox(4)];
+    end
+end
+
+
+
+
+save('menpo_mtcnn.mat', 'bboxes', 'dets', 'gt_labels');
\ No newline at end of file
diff --git a/matlab_version/bounding_box_mapping/learn_mapping_mtcnn_menpo.m b/matlab_version/bounding_box_mapping/learn_mapping_mtcnn_menpo.m
new file mode 100644
index 00000000..132b741e
--- /dev/null
+++ b/matlab_version/bounding_box_mapping/learn_mapping_mtcnn_menpo.m
@@ -0,0 +1,85 @@
+load('menpo_mtcnn.mat')
+
+% Find the ground truth bboxes
+bboxes_gt = bboxes;
+
+bboxes_det = dets;
+
+non_detected = bboxes_det(:,3) == 0;
+
+% Removing the outliers
+widths_gt = bboxes_gt(:,3) - bboxes_gt(:,1);
+widths_det = bboxes_det(:,3) - bboxes_det(:,1);
+
+heights_gt = bboxes_gt(:,4) - bboxes_gt(:,2);
+heights_det = bboxes_det(:,4) - bboxes_det(:,2);
+
+tx_gt =  bboxes_gt(:,1);
+ty_gt =  bboxes_gt(:,2);
+
+tx_det = bboxes_det(:,1);
+ty_det = bboxes_det(:,2);
+
+bad_det_1 = abs(1 - widths_gt ./ widths_det) > 0.5;
+bad_det_2 = abs(1 - heights_gt ./ heights_det) > 0.5;
+
+bad_det_3 = abs((tx_gt - tx_det) ./ widths_det) > 0.4;
+bad_det_4 = abs((ty_gt - ty_det) ./ heights_det) > 0.5;
+
+non_detected = non_detected | bad_det_1 | bad_det_2 | bad_det_3 | bad_det_4;
+
+% if the width is quite different from detection then it failed
+
+bboxes_gt = bboxes_gt(~non_detected,:);
+bboxes_det = bboxes_det(~non_detected,:);
+
+%% some visualisations
+% a = 1;
+% plot(gt_labels(a,:,1), gt_labels(a,:,2), '.r');
+% hold on;
+% bbox = bboxes_detector(a,:);
+% % bbox(2) = -bbox(2);
+% rectangle('Position', bbox);
+% hold off;
+% axis equal;
+
+% Want to find out what scaling and translation would lead to the smallest
+% RMSE error between initialised landmarks and gt landmarks TODO
+
+% Find the width and height mappings
+widths_gt = bboxes_gt(:,3) - bboxes_gt(:,1);
+widths_det = bboxes_det(:,3) - bboxes_det(:,1);
+
+heights_gt = bboxes_gt(:,4) - bboxes_gt(:,2);
+heights_det = bboxes_det(:,4) - bboxes_det(:,2);
+
+s_width = widths_det \ widths_gt;
+s_height = heights_det \ heights_gt;
+
+tx_gt =  bboxes_gt(:,1);
+ty_gt =  bboxes_gt(:,2);
+
+tx_det = bboxes_det(:,1);
+ty_det = bboxes_det(:,2);
+
+s_tx = median((tx_gt - tx_det) ./ widths_det);
+s_ty = median((ty_gt - ty_det) ./ heights_det);
+
+%%
+new_widths = widths_det * s_width;
+new_heights = heights_det * s_height;
+new_tx = widths_det * s_tx + tx_det;
+new_ty = heights_det * s_ty + ty_det;
+
+overlaps = zeros(numel(widths_det), 1);
+new_overlaps = zeros(numel(widths_det), 1);
+
+for i=1:numel(widths_det)
+    bbox_gt = bboxes_gt(i,:);
+    bbox_old = bboxes_det(i,:);
+    overlaps(i) = overlap(bbox_gt, bbox_old);
+    bbox_new = [new_tx(i), new_ty(i), new_tx(i) + new_widths(i), new_ty(i) + new_heights(i)];
+    new_overlaps(i) = overlap(bbox_gt, bbox_new);
+end
+
+fprintf('Orig - %.3f, now - %.3f\n', mean(overlaps), mean(new_overlaps));
diff --git a/matlab_version/bounding_box_mapping/menpo_mtcnn.mat b/matlab_version/bounding_box_mapping/menpo_mtcnn.mat
new file mode 100644
index 00000000..13fdd6bc
Binary files /dev/null and b/matlab_version/bounding_box_mapping/menpo_mtcnn.mat differ
diff --git a/matlab_version/bounding_box_mapping/menpo_train.mat b/matlab_version/bounding_box_mapping/menpo_train.mat
new file mode 100644
index 00000000..94ab7365
Binary files /dev/null and b/matlab_version/bounding_box_mapping/menpo_train.mat differ
diff --git a/matlab_version/bounding_box_mapping/readme.txt b/matlab_version/bounding_box_mapping/readme.txt
index 9e787bf8..5a0a05bb 100644
--- a/matlab_version/bounding_box_mapping/readme.txt
+++ b/matlab_version/bounding_box_mapping/readme.txt
@@ -1 +1,5 @@
-Code here was used to learn the mapping from a face detection bounding box to the bounding boxes suitable for the initialisation of landmark detection.
\ No newline at end of file
+Code here was used to learn the mapping from a face detection bounding box to the bounding boxes suitable for the initialisation of landmark detection.
+
+For extracting the bounding boxes from MTCNN:
+- run collect_transform_mtcnn_menpo.m
+- run learn_mapping_mtcnn_menpo.m
\ No newline at end of file