153 lines
No EOL
4.7 KiB
Matlab
153 lines
No EOL
4.7 KiB
Matlab
clear;
|
|
face_processed_dir = 'E:\datasets\face_datasets_processed';
|
|
|
|
%% CK+
|
|
hog_dir = [face_processed_dir, '/ck+/'];
|
|
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,:);
|
|
|
|
%% Bosphorus
|
|
hog_dir = [face_processed_dir, '/bosph/'];
|
|
hog_files = dir([hog_dir '*.hog']);
|
|
|
|
% Remove non-frontal
|
|
frontal = true(size(hog_files));
|
|
for i = 1:numel(frontal)
|
|
if(~isempty(strfind(hog_files(i).name, 'YR')) || ~isempty(strfind(hog_files(i).name, 'PR'))|| ~isempty(strfind(hog_files(i).name, 'CR')))
|
|
frontal(i) = false;
|
|
end
|
|
end
|
|
|
|
hog_files = hog_files(frontal);
|
|
|
|
[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
|
|
|
|
appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
|
|
vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
|
|
|
|
appearance_data = cat(1,appearance_data, appearance_data_tmp);
|
|
vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
|
|
|
|
%% FERA2011
|
|
hog_dir = [face_processed_dir, '/fera2011/'];
|
|
hog_files = dir([hog_dir '*.hog']);
|
|
[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
|
|
|
|
appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
|
|
vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
|
|
|
|
appearance_data = cat(1,appearance_data, appearance_data_tmp);
|
|
vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
|
|
|
|
%% UNBC
|
|
hog_dir = [face_processed_dir, '/unbc/'];
|
|
hog_files = dir([hog_dir '*.hog']);
|
|
[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
|
|
|
|
appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
|
|
vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
|
|
|
|
appearance_data = cat(1,appearance_data, appearance_data_tmp);
|
|
vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
|
|
|
|
|
|
|
|
%% DISFA
|
|
hog_dir = [face_processed_dir, '/disfa/'];
|
|
hog_files = dir([hog_dir '*.hog']);
|
|
|
|
[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
|
|
|
|
appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
|
|
vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
|
|
|
|
appearance_data = cat(1,appearance_data, appearance_data_tmp);
|
|
vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
|
|
|
|
%% BP4D train
|
|
hog_dir = [face_processed_dir, '/bp4d/train/'];
|
|
hog_files = dir([hog_dir '*.hog']);
|
|
[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
|
|
|
|
appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
|
|
vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
|
|
|
|
appearance_data = cat(1,appearance_data, appearance_data_tmp);
|
|
vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
|
|
|
|
%% SEMAINE train
|
|
hog_dir = [face_processed_dir, '/semaine/train/'];
|
|
hog_files = dir([hog_dir '*.hog']);
|
|
|
|
[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
|
|
|
|
appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
|
|
vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
|
|
|
|
appearance_data = cat(1,appearance_data, appearance_data_tmp);
|
|
vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
|
|
|
|
%%
|
|
means_norm = mean(appearance_data);
|
|
stds_norm = std(appearance_data);
|
|
|
|
normed_data = bsxfun(@times, bsxfun(@plus, appearance_data, -means_norm), 1./stds_norm);
|
|
|
|
%% Creating a generic model
|
|
[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');
|
|
|
|
%% Creating a lower face model
|
|
normed_data_lower_face = normed_data;
|
|
normed_data_lower_face(:, 1:5*12*31) = 0;
|
|
|
|
[PC, score, eigen_vals] = princomp(normed_data_lower_face, 'econ');
|
|
|
|
% Keep 98 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) >= 0.98)
|
|
count = i;
|
|
break;
|
|
end
|
|
end
|
|
|
|
PC = PC(:,1:count);
|
|
|
|
save('generic_face_lower.mat', 'PC', 'means_norm', 'stds_norm');
|
|
|
|
%% Creating an upper face model
|
|
normed_data_upper_face = normed_data;
|
|
normed_data_upper_face(:, end-5*12*31+1:end) = 0;
|
|
|
|
[PC, score, eigen_vals] = princomp(normed_data_upper_face, 'econ');
|
|
|
|
% Keep 98 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) >= 0.98)
|
|
count = i;
|
|
break;
|
|
end
|
|
end
|
|
|
|
PC = PC(:,1:count);
|
|
|
|
save('generic_face_upper.mat', 'PC', 'means_norm', 'stds_norm'); |