2dpca实验

%%该程序主要用于训练样本
%读取文件名为1人的手指静脉图片
file_path='E:\dataset\fv\fingervein2_2\';    %图片文件夹路径
img_path_list=dir(strcat(file_path,'*.bmp'));  %获取图片文件夹下所有bmp格式的图片
img_num=length(img_path_list);              %获取图片总数量
%%
l1=5;
l4=5;

%%
cc=210;
ni=3;
for j=1:cc
    sum1=0;
    for i=1:ni
    image_name=img_path_list(i).name;  %图像名
    image=imread(strcat(file_path,image_name));
    %img1(:,:,i)=rgb2gray(image);
    img1(:,:,i)=image;
    sum1=sum1+double(img1(:,:,i));
    end 
    x(:,:,(j-1)*ni+1:j*ni)=img1(:,:,1:ni);
    m(:,:,j)=sum1/ni;%计算每个手指的平均图像
    [row,low]=size(sum1);
end 
%总均值
sum13=0;
for i=1:cc
    sum13=sum13+m(:,:,i);
end 
averge=sum13/cc;
%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%
%去列相关
sum2=0;
for i=1:ni*cc
    temp1=(double(x(:,:,i))-averge)'*(double(x(:,:,i))-averge);
    sum2=sum2+temp1;
end 
COR1=1/(ni*cc)*sum2;
[v,r]=eig(COR1);
[t1,t2]=size(v);
V1=v(:,t2-l1+1:t2);

sum(sum(r(:,t2-l1+1:t2)))/sum(sum(r));
%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%
%把原数据变换到2dpca中
for i=1:cc*ni
    tem = double(x(:,:,i));
    xx(:,:,i)=tem*V1;
end 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%计算把特征空间的类均值投影到2dpca中
xx(:,:,:)=double(xx(:,:,:));
images=[];
for c=1:cc
    mm(:,:,c)=m(:,:,c)*V1;
end 
aver=averge*V1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%对经过pca变换的数据在行方向上进行fisher变换
for c=1:cc
    sum2=0;
    for i=ni*(c-1)+1:ni*c
temp1=(double((xx(:,:,i)-mm(:,:,c)))*double((xx(:,:,i)-mm(:,:,c)))');
sum2=sum2+temp1;
    end 
end 
    kw=sum2/(ni*cc);
    %把特征空间的总体均值投影到高维空间中
    images=[];
    sum3=0;
    for i=1:cc
        temp2=double((mm(:,:,i)-aver))*double((mm(:,:,i)-aver))';
        sum3=sum3+temp2;
    end 
    kb=sum3/cc;
    rank(kw);
    %求广义逆矩阵得到投影矩阵v
    [v r]=eig(kb,kw);
    t2=size(v);
    V2=v(t2-l4+1:t2,:);
    size(V2);
    sum(sum(r(t2-l4+1:t2,:)))/sum(sum(r));
    %%%%%%%%%%%%%%%%%%%%%%%%%
    %%%%%%%%%%%%%%%%%%%%%%%%%%%
 %各样本均值行和列方向上分别经过pca和fisher变换后作为训练样本的类中心   
    for i=1:cc
        averg(:,:,i)=V2*double(m(:,:,i))*V1;
    end 
    save('E:\dataset\fv\data_train.mat','V1','V2','averg','ni');
    %训练结束
    %err
 
    %该程序用于测试样本的识别
load('E:\dataset\fv\data_train.mat','V1','V2','averg','ni');
%%
file_path='E:\dataset\fv\fingervein2_2\';    %图片文件夹路径
img_path_list=dir(strcat(file_path,'*.bmp'));  %获取图片文件夹下所有bmp格式的图片
%%
%用最小距离分类器进行分类并计算错误率
cc=210;
n=6;
e=0;
for j=1:cc
    for i=ni+1:n
        
image_name=img_path_list(i).name;  %图像名
    image=imread(strcat(file_path,image_name));
    img=image;
    sum10=V2*double(img)*V1;%将待识别的样本投影到识别空间
    dd=[];
    for l=1:cc
        d(l)=norm(sum10-averg(:,:,l)); %计算距离
        dd=[dd d(l)]
    end
    mind=min(dd(1:cc));
    if(d(j)~=mind)
        e=e+1;
    end 
    end 
end

    disp('错分类的样本数为：');
    disp(e);
    disp('识别率；');
    disp(1-e/(cc*(n-ni)));
    
    
      

　　错分样本数为0，分类准确率100%。