lda.rar_LDA 人脸识别_lda

% This code is LDA base face recognition programme. It reads nots(here 5) % faces from ORL database and the rest (noc-nots) are used as test. % LDA_Performance shows the recognition performance. % % In order to be able to run this programme for ORL face database you need % to download the following zip-file. Then copy this code in the folder % which includes the face samples and then run this script. The zip-file is % available at: http://www.mathworks.com/matlabcentral/fileexchange/22466 % % This code has been written in Fall 2008 by me, Gholamreza Anbarjafari (Shahab). % You can use this code for any research and academic purposes and you may % refer to us in your acknowledgement! % % Feel free to contact us for any further information: % {hasan.demirel, shahab.jafari}[AT]emu.edu.tr % http://faraday.ee.emu.edu.tr/shahab % http://faraday.ee.emu.edu.tr/hdemirel % (c) Demirel and Anbarjafari -2009 clc clear all close all clear memory nump=15; %no_of_classes nots=5; %no_of_training_set D=pwd; d=8； % cd([D,' \MATLAB7\matlab\yale_faces']); %[face,MAP]=imread('D:\MATLAB7\matlab\ORL_DataBase\00101.jpg'); %[a,b]=size(face); counter=0; for i=1:nump for j=1:nots if i < 10 file=imread(['D:\MATLAB7\matlab\yale_faces\subject0',num2str(i),'_',num2str(j),'.bmp']); else file=imread(['D:\MATLAB7\matlab\yale_faces\subject',num2str(i),'_',num2str(j),'.bmp']); end [a,b]=size(file); % grayface=ind2gray(face,MAP); counter=counter+1; x(counter,:)=double(reshape(file,a*b,1)); end end counter=0; for i=1:nump for j=nots+1:10 if i < 10 file=imread(['D:\MATLAB7\matlab\yale_faces\subject0',num2str(i),'_',num2str(j),'.bmp']); else file=imread(['D:\MATLAB7\matlab\yale_faces\subject',num2str(i),'_',num2str(j),'.bmp']); end [a,b]=size(file); %grayface=ind2gray(face,MAP); counter=counter+1; y(counter,:)=double(reshape(file,a*b,1)); end end for i=1：d d_sort(i) = d1(index(i));%将正的特征值排序 v_sort(:,i) = V(:,index(i));%特征向量排序 v_sort(:,i) = v_sort(:,i)./sqrt(sum(v_sort(:,i).^2));%求解最优投影向量 end; cd(D) clear memory clc AVERAGE=mean(x')'; Average_Matrix=(ones(a*b,1)*AVERAGE')'; clear memory Difference=double(x)-double(Average_Matrix); [V,L]=eig(Difference'*Difference); clear memory [rr,cc]=size(L); maxL=min(min(L)); for i=1:rr for j=1:cc if L(i,j)>maxL maxL=L(i,j); ii=i; jj=j; end end end v=V(:,jj); Lamda=max(max(L)); clear memory counter=1; for i=1:nump for j=1:nots new_X(counter,:)=v(i,:)'*(x(:,counter))'; counter=counter+1; end end clear memory counter=1; for i=1:nump for j=1:3-nots new_Y(counter,:)=v(i,:)'*(y(:,counter))'; counter=counter+1; end end clear memory counter=0;holder=0; for i=1:nump*(3-nots) error=[]; for j=1:nump*nots temp=(new_X(j,:)-new_Y(i,:)); distance=sqrt(temp*temp'); error=[error distance]; end clear memory Minimum_Error=max(error); for k=1:nump*nots if error(k)<Minimum_Error Minimum_Error=error(k); holder=k; end end if ceil(holder/nots)==ceil(i/(3-nots)) counter=counter+1; end clear memory end clear memory clear new_Y new_X v x y error Minimum_Error LDA_Performance=(counter/(noc*(3-nots)))*100 clear counter