PCAbased Face Recognition System
by
Amir Omidvarnia
20 Oct 2007
(Updated
22 Oct 2007)
This package implements 'Eigenface', a PCAbased face recognition system.

Recognition(TestImage, m, A, Eigenfaces)

function OutputName = Recognition(TestImage, m, A, Eigenfaces)
% Recognizing step....
%
% Description: This function compares two faces by projecting the images into facespace and
% measuring the Euclidean distance between them.
%
% Argument: TestImage  Path of the input test image
%
% m  (M*Nx1) Mean of the training
% database, which is output of 'EigenfaceCore' function.
%
% Eigenfaces  (M*Nx(P1)) Eigen vectors of the
% covariance matrix of the training
% database, which is output of 'EigenfaceCore' function.
%
% A  (M*NxP) Matrix of centered image
% vectors, which is output of 'EigenfaceCore' function.
%
% Returns: OutputName  Name of the recognized image in the training database.
%
% See also: RESHAPE, STRCAT
% Original version by Amir Hossein Omidvarnia, October 2007
% Email: aomidvar@ece.ut.ac.ir
%%%%%%%%%%%%%%%%%%%%%%%% Projecting centered image vectors into facespace
% All centered images are projected into facespace by multiplying in
% Eigenface basis's. Projected vector of each face will be its corresponding
% feature vector.
ProjectedImages = [];
Train_Number = size(Eigenfaces,2);
for i = 1 : Train_Number
temp = Eigenfaces'*A(:,i); % Projection of centered images into facespace
ProjectedImages = [ProjectedImages temp];
end
%%%%%%%%%%%%%%%%%%%%%%%% Extracting the PCA features from test image
InputImage = imread(TestImage);
temp = InputImage(:,:,1);
[irow icol] = size(temp);
InImage = reshape(temp',irow*icol,1);
Difference = double(InImage)m; % Centered test image
ProjectedTestImage = Eigenfaces'*Difference; % Test image feature vector
%%%%%%%%%%%%%%%%%%%%%%%% Calculating Euclidean distances
% Euclidean distances between the projected test image and the projection
% of all centered training images are calculated. Test image is
% supposed to have minimum distance with its corresponding image in the
% training database.
Euc_dist = [];
for i = 1 : Train_Number
q = ProjectedImages(:,i);
temp = ( norm( ProjectedTestImage  q ) )^2;
Euc_dist = [Euc_dist temp];
end
[Euc_dist_min , Recognized_index] = min(Euc_dist);
OutputName = strcat(int2str(Recognized_index),'.jpg');


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