人脸识别_描述人脸之间的相似性_用于人脸也可以识别图像，文字等_matlab

clc; clear all; Class_train_num = 12; %%%%每个类选择的训练样本个数 Class_num = 68; %%%%%类别个数 Class_image_num = 24; %%%%%每个类的图象总数 Class_test_num = Class_image_num-Class_train_num; %%%%%每个类选择的测试样本个数 Train_num = Class_num*Class_train_num; Test_num = Class_num*Class_test_num; t=3e+8; %======================================================================= fid=fopen('E:\jieguo\2DDLPP\PIE.txt','a'); fprintf(fid,'%20s','设置参数:'); fprintf(fid,'%12s','t:'); fprintf(fid,'%12d\n',t); fprintf(fid,'%20s','Class number:'); fprintf(fid,'%12g',Class_num); fprintf(fid,'%20s','Image number:'); fprintf(fid,'%12g',Class_image_num); fprintf(fid,'%20s','Training number:'); fprintf(fid,'%12g\n',Class_train_num); %=================================================================== load('D:\database\Pose29_64x64.mat'); for i = 1 : Class_num for j = 1 : Class_train_num index = (i-1)*Class_train_num+j; image_index=(i-1)*Class_image_num+j; a = double(reshape(fea(image_index,:),[64 64])); train_matrix(:,:,index)=a; clear a; end end %%%%%%%%%%%%%%%%%%%% reading test images %%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear index; clear image_index; clear i; clear j; for i = 1 : Class_num for j = 1 : Class_test_num index = (i-1)*Class_test_num+j; image_index=(i-1)*Class_image_num+Class_train_num+j; b= double(reshape(fea(image_index,:),[64 64])); test_matrix(:,:,index) = b; %%%%%%读入训练图像，维数为row*(Class_num*Class_train_num*column) clear b; end end clear index; clear image_index; clear i; clear j; %%%%%%%每一类的均值 for i = 1 : Class_num Class_mean(:,:,i) = mean(train_matrix(:,:,(i-1)*Class_train_num+1:i*Class_train_num),3); end for i = 1:Class_num f(Class_train_num *(i-1)+1:Class_train_num *i,1)= ones(Class_train_num ,1)*i;%%%类别 end options.gnd = f; %%%%%%%%训练样本间的距离 Euc_Distance=zeros(Train_num,Train_num); for i=1:Train_num-1 for j=i+1:Train_num Euc_Distance(i,j)=norm(train_matrix(:,:,i)-train_matrix(:,:,j),'fro'); %the distance between Xi and Xj end end Euc_Distance=Euc_Distance+Euc_Distance'; clear i j; %%%%%%%%类别间的距离 Class_Euc_Distance=zeros(Class_num,Class_num); for i=1:Class_num-1 for j=i+1:Class_num Class_Euc_Distance(i,j)=norm(Class_mean(:,:,i)-Class_mean(:,:,j),'fro'); %the distance between Xi and Xj end end Class_Euc_Distance=Class_Euc_Distance+Class_Euc_Distance'; clear i j; %%%%%%%判断类别 F = zeros(Train_num,Train_num); Label = unique(options.gnd); nLabel = length(Label); for idx=1:nLabel classIdx = find(options.gnd==Label(idx)); F(classIdx,classIdx) = 1;%%%同类 end %%%%%%%计算2个权重矩阵 S=zeros(Train_num,Train_num); B=zeros(Class_num,Class_num); DDD=exp(-(Euc_Distance.^2)./t); B=exp(-(Class_Euc_Distance.^2)./t); S=F.*DDD; %%%%%%%%% [a,b]=size(train_matrix(:,:,1)'); G=zeros(a,a); GG=zeros(a,a); for i = 1 : Class_num for j = 1 : Class_train_num for k = 1 : Class_train_num L1=train_matrix(:,:,(i-1)*Class_train_num+j)-train_matrix(:,:,(i-1)*Class_train_num+k); G=G+L1'*S(i,j)*L1; end end end for i = 1 : Class_num for j = 1 : Class_num L2=Class_mean(:,:,i)-Class_mean(:,:,j); GG=GG+L2'*B(i,j)*L2; end end [v,d]=eig(G,GG); D2=diag(d); [eigvalues,Index]=sort(D2); eigvectors=v(:,Index); for i=1:Train_num Y(:,:,i)=train_matrix(:,:,i)*eigvectors; %%训练样本的LPP投影矩阵 end for j=1:Test_num Z(:,:,j)=test_matrix(:,:,j)*eigvectors; end clear i j d; FNUM=20; for PNUM = FNUM : -1 : 1 %%%%%%%%%%%%%%%%%% calculating similarity %%% for i=1:Test_num for j=1:Train_num DF = Z(:,1:PNUM,i)-Y(:,1:PNUM,j); % d(j,i)=norm(Z(:,1:PNUM,i)-Y(:,1:PNUM,j),2); d(j,i) = sum(sqrt(diag(DF'*DF))); end end [K,L] = min(d); L = fix((L-1)/Class_train_num+1); fresult =[]; for i =1 :Class_num fresult=[fresult i*ones(1,Class_test_num)]; end error = 0; for i = 1:Test_num if L(i) ~= fresult(i) error = error+1; end end fprintf(fid,'%20s','Feature number:'); fprintf(fid,'%12g',PNUM); fprintf(fid,'%12s','L2'); fprintf(fid,'%12g',(1-error/(Test_num))*100); fprintf(fid,'\r\n%12g',''); end fclose(fid);