%基于Matlab的PCA人脸识别算法
tic
allsamples=[];
for i=1:40
for j=1:5
a=imread(strcat('D:\PCA\ORL人脸库\ORL\',num2str(i,'%03d'),'_',num2str(j,'%03d'),'.bmp'));
b=a(1:112*92);
b=double(b);
allsamples=[allsamples;b];
end
end
samplemean=mean(allsamples);%均值
for i=1:200 xmean(i,:)=allsamples(i,:)-samplemean;
end;
%获取特征植及特征向量
sigma=xmean*xmean';
[v d]=eig(sigma);
d1=diag(d);%为列向量
%按特征值大小以降序排列
dsort=flipud(d1);
vsort=fliplr(v);
%以下选择90%的能量
dsum=sum(dsort);
dsum_extract=0;
p=0;
while(dsum_extract/dsum<0.96)
p=p+1;
dsum_extract=sum(dsort(1:p));
end
i=1;
% (训练阶段)计算特征脸形成的坐标系
base = xmean' * vsort(:,1:p) * diag(dsort(1:p).^(-1/2));
allcoor=allsamples*base;
accu = 0;
%测试过程
for i=1:40
for j=6:10%读入40 x 5 副测试图像
a=imread(strcat('D:\PCA\ORL人脸库\ORL\',num2str(i,'%03d'),'_',num2str(j,'%03d'),'.bmp'));
b=a(1:10304);
b=double(b);
tcoor=b*base;
for k=1:200
mdist(k)=norm(tcoor-allcoor(k,:));
end;
%三阶近邻
[dist,index2]=sort(mdist);
class1=floor( (index2(1)-1)/5 )+1;
class2=floor((index2(2)-1)/5)+1;
class3=floor((index2(3)-1)/5)+1;
if class1~=class2 && class2~=class3
class=class1;
elseif class1==class2
class=class1;
elseif class2==class3
class=class2;
end;
if class==i
accu=accu+1;
end;
end;
end;
toc%time elapce about 15 sec
accuracy=accu/200 % 输出识别率
