人脸识别

      计划实现了一个基于PCA的人脸识别方法，我称之为“特征点方法”，所有的功能简单而且实用。
      下面，我使用一个简单的MATLAB脚本说明它的用法。
      一般情况，你应该按照以下这个顺序执行这个方法：
      1. 选择实际测试和参照组人脸图像数据库的路径；
      2. 选择实际测试人脸图像的路径；
      3. 运行“CreateDatabase”函数来创建所有参照组人脸图像的二维矩阵；
      4. 运行“eigenfacecore"函数产生基础人脸图像空间；
      5. 运行“识别”功能得到参照组人脸图像数据库中等价图像的名称。
      为了您的方便，我准备了实际测试和参照组人脸图像数据库，其中部分来自“Face94”埃塞克斯人脸数据库。
     你只需要复制上述功能，指定实际测试和参照组人脸图像数据库的路径（比如Matlab工作路径）。
     然后按照对话框提示输入图片编号，实例将运行实现。

     希望您能喜欢它！

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.zip

以下为源代码文件：

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CreateDatabase.m

function 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 % construct 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: STRCMP, STRCAT, RESHAPE% Original version by Amir Hossein Omidvarnia, October 2007%                     Email: [e-mail]                  %%%%%%%%%%%%%%%%%%%%%%%% 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,'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 = int2str(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

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EigenfaceCore.m

function [m, A, Eigenfaces] = EigenfaceCore(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, 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 of the training database%                A                      - (M*NxP) Matrix of centered image vectors%% See also: EIG% Original version by Amir Hossein Omidvarnia, October 2007%                     Email: [e-mail]                   %%%%%%%%%%%%%%%%%%%%%%%% Calculating the mean image m = mean(T,2); % Computing the average face image m = (1/P)*sum(Tj's)    (j = 1 : P)Train_Number = size(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 linear 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 calculate 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 both 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(:,i)];    endend%%%%%%%%%%%%%%%%%%%%%%%% Calculating the eigenvectors of covariance matrix 'C'% Eigenvectors of covariance matrix C (or so-called "Eigenfaces")% can be recovered from L's eiegnvectors.Eigenfaces = A * L_eig_vec; % A: centered image vectors

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Recognition.m

function OutputName = Recognition(TestImage, m, A, Eigenfaces)% 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.%%                Eigenfaces             - (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.%% See also: RESHAPE, STRCAT% Original version by Amir Hossein Omidvarnia, October 2007%                     Email: [e-mail]                  %%%%%%%%%%%%%%%%%%%%%%%% 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 = [];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 = 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 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];end[Euc_dist_min , Recognized_index] = min(Euc_dist);OutputName = strcat(int2str(Recognized_index),'.jpg');

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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: [e-mail]                  clear allclcclose all% You can customize and fix initial directory pathsTrainDatabasePath = uigetdir('D:\MATLAB701\work', 'D:\MATLAB701\work\TrainDatabase' );TestDatabasePath = uigetdir('D:\MATLAB701\work', 'D:\MATLAB701\work\TestDatabase');prompt = {'1:'};dlg_title = 'Input of PCA-Based Face Recognition System';num_lines= 1;def = {'1'};TestImage  = inputdlg(prompt,dlg_title,num_lines,def);TestImage = strcat(TestDatabasePath,'\',char(TestImage),'.jpg');im = imread(TestImage);T = CreateDatabase(TrainDatabasePath);[m, A, Eigenfaces] = EigenfaceCore(T);OutputName = Recognition(TestImage, m, A, Eigenfaces);SelectedImage = strcat(TrainDatabasePath,'\',OutputName);SelectedImage = imread(SelectedImage);imshow(im)title('Test Image');figure,imshow(SelectedImage);title('Equivalent Image');str = strcat('Matched image is :  ',OutputName);disp(str)

用到的图像库文件：

TestDatabase

TrainDatabase