File_Read_ORD.zip_PCA 特征提取_pca_人脸识别

clear all; close all; clc; for i = 1:40 for j = 1:10 path = strcat('C:\Users\超级霸怪\Desktop\pca+svm\face\s', num2str(i), '\', num2str(j), '.pgm'); filedata{i,j} = imread(path); %imshow(filedata{i,j}); end end [m,n] = size(filedata{1,1}); num =6;%选5张脸当成样本, for i =1:40 %n_rand = randperm(10);%随机选 for j = 1:num train_pic{(i-1)*num+j}=filedata{i,j}; end for k = 1:(10-num) test_pic{(i-1)*(10-num)+k}=filedata{i,(k+num)}; end end for i = 1:40*num train_face(i,:)=reshape(train_pic{i},1,m*n); end for i=1:40*(10-num) test_face(i,:) = reshape(test_pic{i},1,m*n); end accuracy = zeros(1,30); K = 30; ;%前K个特征值 img = double(train_face); [m1,n1] = size(img); img_mean = mean(img);%每列平均值 img_mean_all = repmat(img_mean,m1,1); Z = img -img_mean_all; T = Z*Z';%协方差矩阵 [V,D]=eigs(T,K);%计算T中最大的前k个特征值与特征向量，D特征值，V特征向量,已从大到小排列 a =V; %b =D; V=Z'*V; %c = V; for i = 1:K%特征向量单位化 l = norm(V(:,i)); V(:,i) = V(:,i)/l; end img_new = Z*V;%低维度下的各个脸的数据 num_train = size(img_new,1);%训练脸总数 num_test = size(test_face,1);%测试脸总数 true_num = 0; for i = 1:num_test pic = double(test_face(i,:)); pic1 = pic - img_mean; pic2 = pic1*V; for j = 1:num_train error(j)= norm(img_new(j,:)-pic2);%求取范数距离 end face_find = find(error<=min(error));%取最小者 %e = face_find; face_find = ceil(face_find/num);%向上取整 表示识别的是第几个人的脸 true_face = ceil(i/(10-num));%真实的是第几个人的脸 if face_find == true_face true_num = true_num+1;%正确分类的个数 end end accuracy = true_num/num_test; % end % figure % t = 1:30 % plot(t,accuracy,'-bo'); % legend('范数'); % xlabel('特征值个数'); % ylabel('识别率');