Eigenface by Santiago Serrano Matlab Code

% Face recognition by Santiago Serrano

clear all
close all
clc
% number of images on your training set.
M=50;

% Chosen std and mean. 
% It can be any number that it is close to the std and mean of most of the images.
um=100;
ustd=80;

% read and show image
S=[];    % img matrix
figure(1);
for i=1:M
str=strcat(int2str(i),'.bmp');    % concatenates two strings that form the name of the image
eval('img=imread(str);');
subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
imshow(img)
if i==3
title('Training set','fontsize',18)
end
drawnow;
[irow icol]=size(img);    % get the number of rows (N1) and columns (N2)
temp=reshape(img',irow*icol,1);    % creates a (N1*N2)x1 vector
S=[S temp];    % S is a N1*N2xM matrix after finishing the sequence
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 and background.
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
figure(2);
for i=1:M
str=strcat(int2str(i),'.jpg');
img=reshape(S(:,i),icol,irow);
img=img';
eval('imwrite(img,str)'); 
subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
imshow(img)
drawnow;
if i==3
title('Normalized Training Set','fontsize',18)
end
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 N1xN2 matrix