My code works in the following manner:

1.First, it obtains several images from the training set

2.After loading these images, we find the normalized faces,mean face and perform several calculation.

3.Next, we ask for the name of an image we want to recognize

4.We then project the input image into the eigenspace, and based on the difference from the eigenfaces we make a decision.

5.Depending on eigen weight vector for each input image we make clusters using kmeans command.

Source code i tried:

clear all
close all
clc
% number of images on your training set.
M=1200;

%Chosen std and mean. 
%It can be any number that it is close to the std and mean of most of the images.
um=60;
ustd=32;

%read and show images(bmp);
S=[];   %img matrix

for i=1:M
    str=strcat(int2str(i),'.jpg');   %concatenates two strings that form the name of the image
    eval('img=imread(str);');


       [irow icol d]=size(img); % get the number of rows (N1) and columns (N2)
       temp=reshape(permute(img,[2,1,3]),[irow*icol,d]);     %creates a (N1*N2)x1 matrix
    S=[S temp];         %X is a N1*N2xM matrix after finishing the sequence
                        %this is our S
end


%Here we change the mean and std of all images. We normalize all images.
%This is done to reduce the error due to lighting conditions.
for i=1:size(S,2)
    temp=double(S(:,i));
    m=mean(temp);
    st=std(temp);
    S(:,i)=(temp-m)*ustd/st+um;
end

%show normalized images

for i=1:M
    str=strcat(int2str(i),'.jpg');
    img=reshape(S(:,i),icol,irow);
    img=img';

end


%mean image;
m=mean(S,2);   %obtains the mean of each row instead of each column
tmimg=uint8(m);   %converts to unsigned 8-bit integer. Values range from 0 to 255
img=reshape(tmimg,icol,irow);    %takes the N1*N2x1 vector and creates a N2xN1 matrix
img=img';       %creates a N1xN2 matrix by transposing the image.

% Change image for manipulation
dbx=[];   % A matrix
for i=1:M
    temp=double(S(:,i));
    dbx=[dbx temp];
end

%Covariance matrix C=A'A, L=AA'
A=dbx';
L=A*A';
% vv are the eigenvector for L
% dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx';
[vv dd]=eig(L);
% Sort and eliminate those whose eigenvalue is zero
v=[];
d=[];
for i=1:size(vv,2)
    if(dd(i,i)>1e-4)
        v=[v vv(:,i)];
        d=[d dd(i,i)];
    end
 end

 %sort,  will return an ascending sequence
 [B index]=sort(d);
 ind=zeros(size(index));
 dtemp=zeros(size(index));
 vtemp=zeros(size(v));
 len=length(index);
 for i=1:len
    dtemp(i)=B(len+1-i);
    ind(i)=len+1-index(i);
    vtemp(:,ind(i))=v(:,i);
 end
 d=dtemp;
 v=vtemp;


%Normalization of eigenvectors
 for i=1:size(v,2)       %access each column
   kk=v(:,i);
   temp=sqrt(sum(kk.^2));
   v(:,i)=v(:,i)./temp;
end

%Eigenvectors of C matrix
u=[];
for i=1:size(v,2)
    temp=sqrt(d(i));
    u=[u (dbx*v(:,i))./temp];
end

%Normalization of eigenvectors
for i=1:size(u,2)
   kk=u(:,i);
   temp=sqrt(sum(kk.^2));
    u(:,i)=u(:,i)./temp;
end


% show eigenfaces;

for i=1:size(u,2)
    img=reshape(u(:,i),icol,irow);
    img=img';
    img=histeq(img,255);

end


% Find the weight of each face in the training set.
omega = [];
for h=1:size(dbx,2)
    WW=[];    
    for i=1:size(u,2)
        t = u(:,i)';    
        WeightOfImage = dot(t,dbx(:,h)');
        WW = [WW; WeightOfImage];
    end
    omega = [omega WW];
end


% Acquire new image
% Note: the input image must have a bmp or jpg extension. 
%       It should have the same size as the ones in your training set. 
%       It should be placed on your desktop
ed_min=[];

srcFiles = dir('G:\newdatabase\*.jpg');  % the folder in which ur images exists
for b = 1 : length(srcFiles)
    filename = strcat('G:\newdatabase\',srcFiles(b).name);
    Imgdata = imread(filename);

        InputImage=Imgdata;

InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]);
temp=InImage;
me=mean(temp);
st=std(temp);
temp=(temp-me)*ustd/st+um;
NormImage = temp;
Difference = temp-m;

p = [];
aa=size(u,2);
for i = 1:aa
    pare = dot(NormImage,u(:,i));
    p = [p; pare];
end


InImWeight = [];
for i=1:size(u,2)
    t = u(:,i)';
    WeightOfInputImage = dot(t,Difference');
    InImWeight = [InImWeight; WeightOfInputImage];
end
noe=numel(InImWeight);


% Find Euclidean distance
e=[];
for i=1:size(omega,2)
    q = omega(:,i);
    DiffWeight = InImWeight-q;
    mag = norm(DiffWeight);
    e = [e mag];

end

ed_min=[ed_min MinimumValue];

theta=6.0e+03;
%disp(e)

z(b,:)=InImWeight;

end


IDX = kmeans(z,5);
clustercount=accumarray(IDX, ones(size(IDX)));

disp(clustercount);

QUESTIONS:

1.It is working fine for M=50(i.e Training set contains 50 images) but not for M=1200(i.e Training set contains 1200 images).It is not showing any error.There is no output.I waited for 10 min still there is no output. I think it is going infinite loop.What is the problem?Where i was wrong?

2.Instead of running the training set everytime how eigen faces generated are stored so that stored eigen faces are used for future face recoginition for a new input image.So it reduces wastage of time.