Continue to Site

Welcome to EDAboard.com

Welcome to our site! EDAboard.com is an international Electronics Discussion Forum focused on EDA software, circuits, schematics, books, theory, papers, asic, pld, 8051, DSP, Network, RF, Analog Design, PCB, Service Manuals... and a whole lot more! To participate you need to register. Registration is free. Click here to register now.

Help me about Face Recognition

Status
Not open for further replies.

earlybird

Banned
Member level 1
Joined
Nov 18, 2006
Messages
32
Helped
0
Reputation
0
Reaction score
0
Trophy points
1,286
Location
Bangladesh
Activity points
0
Hello Friends,
My thesis topic is Face Recognition. I've used Eigenface Method.
For better result what can I do? please help me.



sagar
(KUET=Khulna University of Engineering and Technology.Bangladesh)
 

I'm not expert in that field......but I hear about edge method from one of my friend....you can google it

Best Regards,

Salam
 

How does this idea come into your mind?
 

There is a thesis avaialble of Turk and Pentland
Please go through it ........
I am sending you a sample code from DREXEL UNIVERSITY WEBSITE

Regards

Robin



%DREXEL UNIVERSITY
%Eigenface 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
img=img';
figure(3);
imshow(img);
title('Mean Image','fontsize',18)

% 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
figure(4);
for i=1:size(u,2)
img=reshape(u:),i),icol,irow);
img=img';
img=histeq(img,255);
subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
imshow(img)
drawnow;
if i==3
title('Eigenfaces','fontsize',18)
end
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
InputImage = input('Please enter the name of the image and its extension \n','s');
InputImage = imread(strcat('D:\Documents and Settings\sis26\Desktop\',InputImage));
figure(5)
subplot(1,2,1)
imshow(InputImage); colormap('gray');title('Input image','fontsize',18)
InImage=reshape(double(InputImage)',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
ReshapedImage = m + u:),1:aa)*p; %m is the mean image, u is the eigenvector
ReshapedImage = reshape(ReshapedImage,icol,irow);
ReshapedImage = ReshapedImage';
%show the reconstructed image.
subplot(1,2,2)
imagesc(ReshapedImage); colormap('gray');
title('Reconstructed image','fontsize',18)

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

ll = 1:M;
figure(68)
subplot(1,2,1)
stem(ll,InImWeight)
title('Weight of Input Face','fontsize',14)

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

kk = 1:size(e,2);
subplot(1,2,2)
stem(kk,e)
title('Eucledian distance of input image','fontsize',14)

MaximumValue=max(e) % maximum eucledian distance
MinimumValue=min(e) % minimum eucledian distance
 

Friend,
Thanks a lot.
I've used this type of algorithm. I want another algorithm.
Help me by giving another algorithm. I'll be greatful to u.



regards
sagar.

sea002bd@yahoo.com
 

Status
Not open for further replies.

Part and Inventory Search

Welcome to EDABoard.com

Sponsor

Back
Top