lfwL2.m

num = size(AllFeature1,2);
% F1 = AllFeature1(1:512,:)';
F1 = max(AllFeature1(1:512,:)', AllFeature1(513:1024,:)') / 20;%(1:512,:)
% F1 = bsxfun(@minus, F1, min(F1,[],2));
% F1 = bsxfun(@rdivide, F1, max(F1,[],2));
% F1 = bsxfun(@minus, F1, mean(F1,1));
% F1 = bsxfun(@rdivide, F1, sqrt(sum(F1.^2,2)));
% F1 = bsxfun(@minus,F1,PCAmap.mean);
% F1 = F1 * PCAmap.M;

% F2 = AllFeature2(1:512,:)';
F2 = max(AllFeature2(1:512,:)', AllFeature2(513:1024,:)') / 20;%(1:512,:)
% F2 = bsxfun(@minus, F2, min(F2,[],2));
% F2 = bsxfun(@rdivide, F2, max(F2,[],2));
% F2 = bsxfun(@minus, F2, mean(F2,1));
% F2 = bsxfun(@rdivide, F2, sqrt(sum(F2.^2,2)));
% F2 = bsxfun(@minus,F2,PCAmap.mean);
% F2 = F2 * PCAmap.M;

% F1 = AllFeature1';
% F2 = AllFeature2';
thresh2 = zeros(num,1);
for i = 1:num
%     thresh2(i) = F1(i,:) * mapping.A * F1(i,:)' + F2(i,:) * mapping.A * F2(i,:)' - 2 * F1(i,:) * mapping.G * F2(i,:)';
    thresh2(i) = pdist2(F1(i,:),F2(i,:));
%     thresh2(i) = F1(i,:) * F2(i,:)';
end;
figure;
hist(thresh2(1:3000),500);
figure;
hist(thresh2(3001:end),500);

accuracies = zeros(10,1);
for i=1:10
    test_idx = [(i-1) * 300 + 1 : i*300, (i-1) * 300 + 3001 : i*300 + 3000];
    train_idx = 1:6000;
    train_idx(test_idx) = [];
    bestc=256;
    same_label = ones(6000,1);
    same_label(3001:6000) = 0;
    % predicted_label = predict(double(lfw_label),sparse(thresh2),model);
    cmd = [' -t 0 -h 0'];
    model = svmtrain(same_label(train_idx),thresh2(train_idx),cmd);
    % model = svmtrain(double(sim_label),thresh,cmd);
    [class, accuracy, deci] = svmpredict(same_label(test_idx),thresh2(test_idx),model);
    accuracies(i) = accuracy(1);
end;
mean(accuracies)
% cmd = [' -t 0 -h 0'];
% model = svmtrain(same_label,thresh2,cmd);
% [class, accuracy, deci] = svmpredict(same_label,thresh2,model);