matlab_2DPCA二维主成分分析的人脸识别，在ORL人脸库上进行了测试

C=40; %样本的类别 t=3; %用于训练的每类样本数 Ni=10; %每类的样本数 all_train=C*t; %训练样本总数 all_test=C*Ni-all_train; L=112*92; %样本维数 D=3; %映射矩阵维数112*D experiment=1; %实验次数 number=zeros(experiment,Ni);%每行前t个用于训练，其余（Ni-t)个用于测试 for rr=1:experiment number(rr,:)=randperm(Ni);%随机产生用于训练和测试的样本序号 end %------------input train sample------------------- train=cell(1,all_train); train_Y=cell(1,all_train); meanrate=0;%10次试验的平均识别率 fprintf('每类 训练样本个数：%2d ',t) ; fprintf('每类 测试样本个数：%2d\n',Ni-t); for e=1:experiment fprintf('第 %2d 组实验 不同维数的识别率\n',e); a=1;%用来计数 class=[]; for i=1:C for j=1:t %每类的前t个样本作为训练样本 str=strcat('D:\Program Files\MATLAB\R2008a\work\facial expression\Face_re\src\ORL\s',int2str(i),'\',int2str(number(e,j)),'.pgm'); eval('img=imread(str);'); train{:,a}=double(img); a=a+1; class=[class i]; end end %------------[eigvector,eigvalue]=TPCA(A,gnd);------------ ratematrix=[]; [eigenvector,eigenvalue]=TPCA(train); eigvalue=diag(eigenvalue); [Sr,index]=sort(-eigvalue); for dim=1:D vector=eigenvector(:,index(1:dim)); vector=orth(vector); for c=1:all_train train_Y{1,c}=train{:,c}*vector; end %------------input test sample and compute the rate------------------- success=0; fail=0; for n=(t+1):Ni %number :(t+1)---Ni做测试 for j=1:C test=double(imread(strcat('D:\Program Files\MATLAB\R2008a\work\facial expression\Face_re\src\ORL\s',int2str(j),'\',int2str(number(e,n)),'.pgm'))); test_Y=test*vector;%将测试样本投影 %-------------comput Euclidean distance-------------- distance=[]; for m=1:all_train tt=double(test_Y)-double(train_Y{:,m}); d=0; for di=1:dim everydis=norm(tt(:,di)); d=d+everydis; end distance=[distance d]; end %--------find the minimum distance ----------------- min_num=find(distance==min(distance)); predict=floor((min_num-1)/t)+1; if predict==j success=success+1; else fail=fail+1; %fprintf('%d.jpg fails to match!\n',j); end end end rate=(success/all_test)*100; ratematrix=[ratematrix rate/100]; fprintf('dim:%2d',dim); fprintf(' rate:%4.2f%%',rate); fprintf(' success:%2d',success); fprintf(' fail:%2d',fail); fprintf(' total:%2d\n',all_test); end meanrate=meanrate+ratematrix/experiment; end colum=size(meanrate,2); fprintf('每类 训练样本个数：%2d ',t) ; fprintf('每类 测试样本个数：%2d\n',Ni-t); fprintf('%2d组实验，不同维数的平均识别率\n',experiment); for dim=1:colum fprintf('dim:%2d',dim); fprintf(' meanrate:%4.4f\n',meanrate(:,dim)); end