38 lines
1.3 KiB
Matlab
Executable File
38 lines
1.3 KiB
Matlab
Executable File
function [matched_individual,bestmatchdistance]=eigenfaces_classify(test,training,n);
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%classifies using n principal components, closest match
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[w, h, nExpressions, nIndividuals]=size(training);
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X=reshape(training,[w*h,nIndividuals*nExpressions]);
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avg=mean(X,2);
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Xd=bsxfun(@minus,X,avg);
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[U,S,V]=svd(Xd,0);
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Xt=reshape(test,w*h,numel(test)/(w*h));
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Xtd=bsxfun(@minus,Xt,avg);
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scores=U(:,1:n)'*Xtd;
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trainingscores=U(:,1:n)'*Xd;
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%normalize scores and samples
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%scores=bsxfun(@rdivide,scores,sqrt(sum(abs(scores).^2)));
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%trainingscores=bsxfun(@rdivide,trainingscores,sqrt(sum(abs(trainingscores).^2)));
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%cosine similarity
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%C=scores'*trainingscores;
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%[bestmatchdistance bestmatchindex]=max(C,[],2);
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%matched_individual=ceil(bestmatchindex/nExpressions);
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%Euclidean distance
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distanceMatrix=nan(size(scores,2),size(trainingscores,2));
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for i=1:size(scores,2)
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for j=1:size(trainingscores,2)
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distanceMatrix(i,j)=norm(scores(:,i)-trainingscores(:,j));
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end
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end
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[bestmatchdistance bestmatchindex]=min(distanceMatrix,[],2);
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matched_individual=ceil(bestmatchindex/nExpressions);
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if numel(test)==w*h
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subplot(1,2,1);
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imagesc(test);
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colormap(gray);
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subplot(1,2,2);
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imagesc(reshape(X(:,bestmatchindex),[w,h]));
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colormap(gray);
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disp('best match distance=');
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disp(bestmatchdistance);
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end |