1、計(jì)劃實(shí)現(xiàn)了一個(gè)基于PCA的人臉識(shí)別方法，我稱之為“特征點(diǎn)方法”，所有的功能簡(jiǎn)單而且實(shí)用。 下面，我使用一個(gè)簡(jiǎn)單的MATLAB腳本說(shuō)明它的用法。 一般情況，你應(yīng)該按照以下這個(gè)順序執(zhí)行這個(gè)方法： 1.選擇實(shí)際測(cè)試和參照組人臉圖像數(shù)據(jù)庫(kù)的路徑； 2.選擇實(shí)際測(cè)試人臉圖像的路徑； 3.運(yùn)行“CreateDatabase”函數(shù)來(lái)創(chuàng)建所有參照組人臉圖像的二維矩陣； 4.運(yùn)行“eigenfacecore函數(shù)產(chǎn)生基礎(chǔ)人臉圖像空間； 5.運(yùn)行“識(shí)別”功能得到參照組人臉圖像數(shù)據(jù)庫(kù)中等價(jià)圖像的名稱。 為了您的方便，我準(zhǔn)備了實(shí)際測(cè)試和參照組人臉圖像數(shù)據(jù)庫(kù)，其中部分來(lái)自“Face94”埃塞克斯人臉數(shù)據(jù)庫(kù)。 你只需要復(fù)制

2、上述功能，指定實(shí)際測(cè)試和參照組人臉圖像數(shù)據(jù)庫(kù)的路徑（比如Matlab工作路徑）。 然后按照對(duì)話框提示輸入圖片編號(hào)，實(shí)例將運(yùn)行實(shí)現(xiàn)。 希望您能喜歡它！ 引用：1 P. N. Belhumeur, J. Hespanha, and D. J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In ECCV (1), pages 45-58, 1996.2 Available at: http:/cswww.essex.ac.uk/mv/allfaces/faces94

3、.zip以下為源代碼文件：-CreateDatabase.mfunction T = CreateDatabase(TrainDatabasePath)% Align a set of face images (the training set T1, T2, . , TM )% Description: This function reshapes all 2D images of the training database% into 1D column vectors. Then, it puts these 1D column vectors in a row to% construc

4、t 2D matrix T.% % Argument: TrainDatabasePath - Path of the training database% Returns: T - A 2D matrix, containing all 1D image vectors.% Suppose all P images in the training database% have the same size of MxN. So the length of 1D% column vectors is MN and T will be a MNxP 2D matrix.% See also: ST

5、RCMP, STRCAT, RESHAPE% Original version by Amir Hossein Omidvarnia, October 2007% Email: aomidvarece.ut.ac.ir % File managementTrainFiles = dir(TrainDatabasePath);Train_Number = 0;for i = 1:size(TrainFiles,1) if not(strcmp(TrainFiles(i).name,.)|strcmp(TrainFiles(i).name,.)|strcmp(TrainFiles(i).name,

6、Thumbs.db) Train_Number = Train_Number + 1; % Number of all images in the training database endend% Construction of 2D matrix from 1D image vectorsT = ;for i = 1 : Train_Number % I have chosen the name of each image in databases as a corresponding % number. However, it is not mandatory! str = int2st

7、r(i); str = strcat(,str,.jpg); str = strcat(TrainDatabasePath,str); img = imread(str); img = rgb2gray(img); irow icol = size(img); temp = reshape(img,irow*icol,1); % Reshaping 2D images into 1D image vectors T = T temp; % T grows after each turn end-EigenfaceCore.mfunction m, A, Eigenfaces = Eigenfa

8、ceCore(T)% Use Principle Component Analysis (PCA) to determine the most% discriminating features between images of faces.% Description: This function gets a 2D matrix, containing all training image vectors% and returns 3 outputs which are extracted from training database.% Argument: T - A 2D matrix,

9、 containing all 1D image vectors.% Suppose all P images in the training database% have the same size of MxN. So the length of 1D% column vectors is M*N and T will be a MNxP 2D matrix.% Returns: m - (M*Nx1) Mean of the training database% Eigenfaces - (M*Nx(P-1) Eigen vectors of the covariance matrix

10、of the training database% A - (M*NxP) Matrix of centered image vectors% See also: EIG% Original version by Amir Hossein Omidvarnia, October 2007% Email: aomidvarece.ut.ac.ir % Calculating the mean imagem = mean(T,2); % Computing the average face image m = (1/P)*sum(Tjs) (j = 1 : P)Train_Number = siz

11、e(T,2);% Calculating the deviation of each image from mean imageA = ; for i = 1 : Train_Number temp = double(T(:,i) - m; % Computing the difference image for each image in the training set Ai = Ti - m A = A temp; % Merging all centered imagesend% Snapshot method of Eigenface methos% We know from lin

12、ear algebra theory that for a PxQ matrix, the maximum% number of non-zero eigenvalues that the matrix can have is min(P-1,Q-1).% Since the number of training images (P) is usually less than the number% of pixels (M*N), the most non-zero eigenvalues that can be found are equal% to P-1. So we can calc

13、ulate eigenvalues of A*A (a PxP matrix) instead of% A*A (a M*NxM*N matrix). It is clear that the dimensions of A*A is much% larger that A*A. So the dimensionality will decrease.L = A*A; % L is the surrogate of covariance matrix C=A*A.V D = eig(L); % Diagonal elements of D are the eigenvalues for bot

14、h L=A*A and C=A*A.% Sorting and eliminating eigenvalues% All eigenvalues of matrix L are sorted and those who are less than a% specified threshold, are eliminated. So the number of non-zero% eigenvectors may be less than (P-1).L_eig_vec = ;for i = 1 : size(V,2) if( D(i,i)1 ) L_eig_vec = L_eig_vec V(

15、:,i); endend% Calculating the eigenvectors of covariance matrix C% Eigenvectors of covariance matrix C (or so-called Eigenfaces)% can be recovered from Ls eiegnvectors.Eigenfaces = A * L_eig_vec; % A: centered image vectors-Recognition.mfunction OutputName = Recognition(TestImage, m, A, Eigenfaces)%

16、 Recognizing step.% Description: This function compares two faces by projecting the images into facespace and% measuring the Euclidean distance between them.% Argument: TestImage - Path of the input test image% m - (M*Nx1) Mean of the training% database, which is output of EigenfaceCore function.% E

17、igenfaces - (M*Nx(P-1) Eigen vectors of the% covariance matrix of the training% database, which is output of EigenfaceCore function.% A - (M*NxP) Matrix of centered image% vectors, which is output of EigenfaceCore function.% Returns: OutputName - Name of the recognized image in the training database

18、.% See also: RESHAPE, STRCAT% Original version by Amir Hossein Omidvarnia, October 2007% Email: aomidvarece.ut.ac.ir % Projecting centered image vectors into facespace% All centered images are projected into facespace by multiplying in% Eigenface basiss. Projected vector of each face will be its cor

19、responding% feature vector.ProjectedImages = ;Train_Number = size(Eigenfaces,2);for i = 1 : Train_Number temp = Eigenfaces*A(:,i); % Projection of centered images into facespace ProjectedImages = ProjectedImages temp;end% Extracting the PCA features from test imageInputImage = imread(TestImage);temp

20、 = InputImage(:,:,1);irow icol = size(temp);InImage = reshape(temp,irow*icol,1);Difference = double(InImage)-m; % Centered test imageProjectedTestImage = Eigenfaces*Difference; % Test image feature vector% Calculating Euclidean distances% Euclidean distances between the projected test image and the

21、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 q = ProjectedImages(:,i); temp = ( norm( ProjectedTestImage - q ) )2; Euc_dist = Euc_dist temp;endE

22、uc_dist_min , Recognized_index = min(Euc_dist);OutputName = strcat(int2str(Recognized_index),.jpg);-example.m% A sample script, which shows the usage of functions, included in% PCA-based face recognition system (Eigenface method)% See also: CREATEDATABASE, EIGENFACECORE, RECOGNITION% Original version by Amir Hossein Omidvarnia, October 2007% Email: aomidvarece.ut.ac.ir clear allclcclose all% You can customize and fix initial directory pathsTrainDatabasePath = uigetdir(D:MATLAB