人脸识别PCA

function [ correct_rate,mean_face_image,eigen_face_5 ] = FaceRecognition( k ) clear all; % 训练阶段 cols = 92; rows = 112; train_samples = []; sample_size = 40 * 7; for i = 1:40 for j = 1: 7 img = imread(strcat('att_faces/s',num2str(i),'/',num2str(j),'.pgm')); %imshow(img); img_vec = img(:); img_vec = im2double(img_vec); train_samples = [train_samples img_vec]; end end mean_face = mean(train_samples,2); mean_face_image = reshape(mean_face,rows,cols); dif_face = train_samples - mean_face; % 协方差矩阵 cov = dif_face' * dif_face / (sample_size - 1); % 计算特征值与特征向量 [v, d] = eig(cov); % 提取特征值 eigenvalue = diag(d); % 特征值排序和特征向量排序 eigenvalue_sort = flipud(eigenvalue); eigenvector_sort = fliplr(v); % eigenvalue_sum = sum(eigenvalue); % extract_value = 0; % p = 0; % while (extract_value / eigenvalue_sum < 0.90) % p = p + 1; % extract_value = sum(eigenvalue_sort(1:p)); % end w = []; for i = 1:k u = eigenvalue_sort(i,1)^(-1/2) * dif_face * eigenvector_sort(:,i); w = [w u]; if (i == 24) eigen_face_5 = w; end end eigen_face = w' * dif_face ; % 识别阶段 correct = 0; for i = 1:40 for j = 8:10 img = imread(strcat('att_faces/s',num2str(i),'/',num2str(j),'.pgm')); img = im2double(img(:)); eigen_space = w' * (img - mean_face); e = []; for t = 1:280 e(1,t) = norm(eigen_face(:,t) - eigen_space,2); end [~,reg] = min(e); if (ceil(reg / 7) == i) correct = correct + 1; end end end testSample_size = 120; correct_rate = correct / testSample_size; end