PCA人脸识别matlab代码，包括测试数据集

function OutputName = Recognition(TestImagePath, MeanFace, MeanNormFaces, EigenFaces) % Description: This function compares two faces by projecting the images into facespace and % measuring the Euclidean distance between them. % Input: TestImagePath - Path of test face image % MeanFace -(1*MN) mean vector, which is one % of the output of 'EigenfaceCore.m' % % MeanNormFaces -(P*MN) matrix with each row % represents a mean-normalized % face, which is one of the output % of 'EigenfaceCore.m' % EigenFaces %%%%%%%%%%%%%%%%%%%%%%%% Projecting centered image vectors into facespace % All centered images are projected into facespace by multiplying in % Eigenface basis's. Projected vector of each face will be its corresponding % feature vector. ProjectedImages = []; % I think here should be the number of centered training faces rather than % number of eigenfaces Train_Number = size(MeanNormFaces,1); for i = 1 : Train_Number temp = (EigenFaces*MeanNormFaces(i,:)')'; % Projection of centered images into facespace ProjectedImages(i,:) =temp; % each row corresponds to a feature end %%%%%%%%%%%%%%%%%%%%%%%% Extracting the PCA features from test image InputImage = imread(TestImagePath); VecInput=reshape(InputImage,1,size(InputImage,1)*size(InputImage,2)); MeanNormInput = double(VecInput)-MeanFace; % Centered test image ProjectedTestImage = (EigenFaces*MeanNormInput')'; % Test image feature vector %%%%%%%%%%%%%%%%%%%%%%%% Calculating Euclidean distances % Euclidean distances between the projected test image and the projection % of all centered training images are calculated. Test image is % supposed to have minimum distance with its corresponding image in the % training database. Euc_dist = []; for i = 1 : Train_Number temp = ( norm( ProjectedTestImage - ProjectedImages(i,:) ) )^2; Euc_dist = [Euc_dist temp]; end [Euc_dist_min , Recognized_index] = min(Euc_dist); OutputName = strcat('s',int2str((Recognized_index-1)/5+1),'class'); figure, subplot(121); imshow(InputImage,[]); title('输入人脸'); subplot(122); imshow(reshape((MeanNormFaces(Recognized_index,:)+MeanFace),112,92),[]); title(strcat('最相似人脸，类别:',int2str((Recognized_index-1)/5+1)));