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Recognition.m
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Recognition.m
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function OutputName = Recognition(InputImage, m, A, Eigenfaces)
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 = rgb2gray(InputImage);
temp = imread(InputImage);
img = imresize(temp,[64 64]);
%[irow icol] = size(temp);
A = img;
k1=Glet(A);
k11=abs(k1);
Input_Im = img;
R=1;
LBP_Im = LBP(Input_Im, R);
c1=k11(:);
c2=LBP_Im(:);
InImage = vertcat(c1,c2);
Difference = double(InImage)-m; % Centered test image
ProjectedTestImage = Eigenfaces'*Difference; % Test image feature vector
%%%%%%%%%%%%%%%%%%%%%%%% Calculating Euclidean distances
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');