2128589309
- 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
120 lines
No EOL
3.6 KiB
Matlab
120 lines
No EOL
3.6 KiB
Matlab
clear;
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%% CK+, FERA2011, and UNBC datasets
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hog_dir = 'D:\Datasets/face_datasets/hog_aligned_rigid/';
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hog_files = dir([hog_dir, '*.hog']);
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[appearance_data, valid_inds, vid_ids_train] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data = appearance_data(valid_inds,:);
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vid_ids_train = vid_ids_train(valid_inds,:);
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%% Bosphorus dataset
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hog_dir = 'D:\Datasets/face_datasets/hog_aligned_rigid_b/';
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hog_files = dir([hog_dir, '*.hog']);
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[appearance_data_bosph, valid_inds, vid_ids_train_bosph] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data_bosph = appearance_data_bosph(valid_inds,:);
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vid_ids_train_bosph = vid_ids_train_bosph(valid_inds,:);
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appearance_data = cat(1,appearance_data, appearance_data_bosph);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_bosph);
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%% DISFA
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hog_dir = 'D:\Datasets\DISFA\hog_aligned_rigid/';
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hog_files = dir([hog_dir, '*.hog']);
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[appearance_data_disfa, valid_inds, vid_ids_train_disfa] = Read_HOG_files_small(hog_files, hog_dir, 100);
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appearance_data_disfa = appearance_data_disfa(valid_inds,:);
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vid_ids_train_disfa = vid_ids_train_disfa(valid_inds,:);
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appearance_data = cat(1,appearance_data, appearance_data_disfa);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_disfa);
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%% BP4D
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hog_dir = 'D:\Datasets\FERA_2015\bp4d\processed_data/train/';
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hog_files = dir([hog_dir, '*.hog']);
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[appearance_data_bp, valid_inds, vid_ids_train_bp] = Read_HOG_files_small(hog_files, hog_dir, 50);
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appearance_data_bp = appearance_data_bp(valid_inds,:);
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vid_ids_train_bp = vid_ids_train_bp(valid_inds,:);
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appearance_data = cat(1,appearance_data, appearance_data_bp);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_bp);
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%% SEMAINE
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hog_dir = 'D:\Datasets\FERA_2015\semaine\processed_data\train\';
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hog_files = dir([hog_dir, '*.hog']);
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[appearance_data_semaine, valid_inds, vid_ids_train_semaine] = Read_HOG_files_small(hog_files, hog_dir, 300);
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appearance_data_semaine = appearance_data_semaine(valid_inds,:);
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vid_ids_train_semaine = vid_ids_train_semaine(valid_inds,:);
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appearance_data = cat(1,appearance_data, appearance_data_semaine);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_semaine);
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%%
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means_norm = mean(appearance_data);
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stds_norm = std(appearance_data);
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normed_data = bsxfun(@times, bsxfun(@plus, appearance_data, -means_norm), 1./stds_norm);
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%% Creating a generic model
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[PC, score, eigen_vals] = princomp(normed_data, 'econ');
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% Keep 95 percent of variability
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total_sum = sum(eigen_vals);
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count = numel(eigen_vals);
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for i=1:numel(eigen_vals)
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if ((sum(eigen_vals(1:i)) / total_sum) >= 0.95)
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count = i;
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break;
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end
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end
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PC = PC(:,1:count);
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save('generic_face_rigid.mat', 'PC', 'means_norm', 'stds_norm');
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%% Creating a lower face model
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normed_data_lower_face = normed_data;
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normed_data_lower_face(:, 1:5*12*31) = 0;
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[PC, score, eigen_vals] = princomp(normed_data_lower_face, 'econ');
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% Keep 98 percent of variability
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total_sum = sum(eigen_vals);
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count = numel(eigen_vals);
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for i=1:numel(eigen_vals)
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if ((sum(eigen_vals(1:i)) / total_sum) >= 0.98)
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count = i;
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break;
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end
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end
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PC = PC(:,1:count);
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save('generic_face_lower.mat', 'PC', 'means_norm', 'stds_norm');
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%% Creating an upper face model
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normed_data_upper_face = normed_data;
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normed_data_upper_face(:, end-5*12*31+1:end) = 0;
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[PC, score, eigen_vals] = princomp(normed_data_upper_face, 'econ');
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% Keep 98 percent of variability
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total_sum = sum(eigen_vals);
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count = numel(eigen_vals);
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for i=1:numel(eigen_vals)
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if ((sum(eigen_vals(1:i)) / total_sum) >= 0.98)
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count = i;
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break;
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end
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end
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PC = PC(:,1:count);
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save('generic_face_upper.mat', 'PC', 'means_norm', 'stds_norm'); |