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From: yonnas syams on 19 Jun 2008 10:41
Hi everyone,
I need help in Minimum Average Correlation Energy Filter
(MACE)filter design.I am trying to make it according to
http://s.i-techonline.com/Book/Face-Recognition/ISBN978-3-902613-03-5-fr26.pdf

I think my program doesn't work well.When I apply it to the
training image used, it's true that there is peak at the
correlation output (positive or negative) peak.But when I
apply the testing image(from the same class/person), there
is no peak at all, and i think there's should be a peak.
One more question,how to normalized the correlation output
so in will be in range -1 to 1.
Any help will be greatly appreciated.

Here is my codes:
%Filter Design
% 3 iris input training , N=3
ans1=imread('001_1_1.bmp');
ans2=imread('001_1_2.bmp');
ans3=imread('001_1_3.bmp');
%change to domain frequency x[d1 d2]
x1=fft2(im2double((ans1.hasil));
x2=fft2(im2double((ans2.hasil));
x3=fft2(im2double((ans3.hasil));
%change to vector kolom X[d 1]
X1 =x1(:);
X2 =x2(:);
X3 =x3(:);
%matrix X[d N]
X=[X1 X2 X3];
%the output i wanted, 1 for authentic image
u=[1 1 1]';
[d N]=size(X);
%compute D, in the paper D is diagonal matrix, but not here
because the result is the same
D1 =X1 .* X1;
D2 =X2 .* X2;
D3 =X3 .* X3;
D =(1/N*d) * (D1+D2+D3) ;
%make inverse D
for i=1:d
D_inv(i,1)=1/D(i,1);
end
%a= inverse(ctranspose X[N d] * inverse D[d 1] * X[d N])
%temp[d N]=inv D[d 1] * X[d N]
for i=1:d
for j=1:N
temp(i,j) = D_inv(i,1) * X(i,j);
end
end
%a[N N]=inv(Xt[N d] * temp[d N]);
a =inv(ctranspose(X) * temp);
%h[d 1]=(inverse D[d 1] * X[d N])[d N] * a[N N] * u[N 1];
h =temp * a * u;
%mace filter in domain frequency
%change to the original size [d1 d2]
[d1 d2]=size(x1);
Hmace=reshape(h,d1,d2);
save Hmace;


%Aplly to Image
iris=imread('001_2_1.bmp');
load Hmace;
x=fft2(im2double(iris));
c=ifft2(x .* Hmace);
figure, mesh(c)

Note : My english is not so good, i am sorry ^_^
From: hulijo on 30 Jun 2008 10:12
Hy yonnas,
It seems that I am having a similar problem trying to implement the MACE
filter. I am quite sure my code is wright, but the results I get when using
MACE correlation filters for recognition are nothing like those reported in
the literature. I achieve lets say a equal error rate of 8% in my experiments
which, however, is comparable only to PCA not even to LDA or other
discriminative methods.

I suspect two things, at least what face recognition is concerned. First,
most papers use a database were pleanty of images per client are available
for training, while I use only three. And second they perform identitfication
experiments, while I do verification. Anyhow, my code for constructing MACE
filters is a bit different from yours as I use training images of all
subjects to construct the filters.

The code:

%read files in the list called "ucn1" and do zero-mean-unit-variance
normalization
vector_size=10000;
number_of_training_images = 600;
Y=[];
for i=1:number_of_training_images
X = double(imread(ucn1(i,:)));
x = reshape(X,[vector_size,1]);
means = mean(x);
vari = sqrt((sum((x-means).^2))/vector_size);
x = (x-means)/vari;
Y=[Y,x];
end
Y=Y';

%transfrom to frequenca domain
im_hight = 100;
im_width = 100;
for i=1:number_of_training_images
X = reshape(Y(i,:),[im_hight,im_width]);
X_f = fft2(X);
X_f = reshape(X_f,[vector_size,1]);
Y_f(i,:)=X_f';
end
Y_f = Y_f';

% MACE filter with all training images, result in "h"
counter=1;
X2 = Y_f;
D=mean((abs(X2)),2));
faktor = repmat(1./D,1,number_of_training_images).*X2*inv((X2)'*(repmat(1./D,
1,number_of_training_images).*X2));
for i=1:3:number_of_training_images %I have three images per
subject
c=zeros(number_of_training_images,1);
c(i:i+2,1)=1;
h(:,counter) = (faktor*c);
counter=counter+1;
end

%h now contains all the filters



%for a test image
X = double(imread(ucn1(1,:)));
x = reshape(X,[vector_size,1]);
means = mean(x);
vari = sqrt((sum((x-means).^2))/vector_size);
x = (x-means)/vari;
X=reshape(x,[im_hight,im_width]);
out = real( ifftshift((ifft2(((fft2(X)).*(reshape(conj(h(:,1)),[im_hight,
im_width])))))));



I hope it the code will help you!

Regards,

hulijo


yonnas syams wrote:
>Hi everyone,
>I need help in Minimum Average Correlation Energy Filter
>(MACE)filter design.I am trying to make it according to
>http://s.i-techonline.com/Book/Face-Recognition/ISBN978-3-902613-03-5-fr26.pdf
>
>I think my program doesn't work well.When I apply it to the
>training image used, it's true that there is peak at the
>correlation output (positive or negative) peak.But when I
>apply the testing image(from the same class/person), there
>is no peak at all, and i think there's should be a peak.
>One more question,how to normalized the correlation output
>so in will be in range -1 to 1.
>Any help will be greatly appreciated.
>
>Here is my codes:
>%Filter Design
>% 3 iris input training , N=3
>ans1=imread('001_1_1.bmp');
>ans2=imread('001_1_2.bmp');
>ans3=imread('001_1_3.bmp');
>%change to domain frequency x[d1 d2]
>x1=fft2(im2double((ans1.hasil));
>x2=fft2(im2double((ans2.hasil));
>x3=fft2(im2double((ans3.hasil));
>%change to vector kolom X[d 1]
>X1 =x1(:);
>X2 =x2(:);
>X3 =x3(:);
>%matrix X[d N]
>X=[X1 X2 X3];
>%the output i wanted, 1 for authentic image
>u=[1 1 1]';
>[d N]=size(X);
>%compute D, in the paper D is diagonal matrix, but not here
>because the result is the same
>D1 =X1 .* X1;
>D2 =X2 .* X2;
>D3 =X3 .* X3;
>D =(1/N*d) * (D1+D2+D3) ;
>%make inverse D
>for i=1:d
> D_inv(i,1)=1/D(i,1);
>end
>%a= inverse(ctranspose X[N d] * inverse D[d 1] * X[d N])
>%temp[d N]=inv D[d 1] * X[d N]
>for i=1:d
> for j=1:N
> temp(i,j) = D_inv(i,1) * X(i,j);
> end
>end
>%a[N N]=inv(Xt[N d] * temp[d N]);
>a =inv(ctranspose(X) * temp);
>%h[d 1]=(inverse D[d 1] * X[d N])[d N] * a[N N] * u[N 1];
>h =temp * a * u;
>%mace filter in domain frequency
>%change to the original size [d1 d2]
>[d1 d2]=size(x1);
>Hmace=reshape(h,d1,d2);
>save Hmace;
>
>%Aplly to Image
>iris=imread('001_2_1.bmp');
>load Hmace;
>x=fft2(im2double(iris));
>c=ifft2(x .* Hmace);
>figure, mesh(c)
>
>Note : My english is not so good, i am sorry ^_^

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