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Why is the accuracy of 0%? MATLAB LIBSVM

I extracted the PCA functions using:

function [mn,A1,A2,Eigenfaces] = pca(T,f1,nf1) m=mean(T,2), %T is the whole training set train=size(T,2); A=[]; for i=1:train temp=double(T(:,i))-m; A=[A temp]; end train=size(f1,2); %f1 - Face 1 images from training set 'T' A=[]; for i=1:train temp=double(f1(:,i))-m; A1=[A1 temp]; end train=size(nf1,2); %nf1 - Images other than face 1 from training set 'T' A=[]; for i=1:train temp=double(nf1(:,i))-m; A2=[A2 temp]; end L=A'*A; [VD]=eig(L); for i=1:size(V,2) if(D(i,i)>1) L_eig=[L_eig V(:,1)]; end end Eigenfaces=A*L_eig; end

Then I projected only face 1 (grade +1) from the training data as such:

Function 1

 for i=1:15 %number of images of face 1 in training set temp=Eigenfaces'*A1(:,i); proj_img1=[proj_img1 temp]; end

Then I project the rest of the faces (class -1) from the training data as such:

Function 2

  for i=1:221 %number of images of faces other than face 1 in training set temp=Eigenfaces'*A2(:,i); proj_img2=[proj_img2 temp]; end

Function 3 Then, the input image vector was obtained using:

 diff=double(inputimg)-mn; %mn is the mean of training data testfeaturevector=Eigenfaces'*diff;

I wrote the results of function 1 and 2 in a CSV file with labels +1 and -1, respectively. Then I used LIBSVM to get the accuracy when providing the true label, it returned 0%, and when I tried to predict the label, it was -1 instead of +1.

And accuracy is approaching like 0%?

Basically, my model is not trained properly, and I see no error.

Any suggestions would be highly appreciated.

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5 answers

Use Eigenfaces as a set of workouts, create a label vector with 1 or -1s (if the i-th column of Eigenfaces refers to 1, then the i-th element in the label is 1, otherwise it is -1). And use the Eigenfaces and label in svmtrain .

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@ lennon310:

 for i=1:length(Eigenfaces) temp=Eigenfaces'*A(:,i); proj_imgs=[proj_imgs temp]; end
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@ lennon310:

  diff=double(inputimg)-mn; %mn is the mean of training data testfeaturevector=Eigenfaces'*diff;
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Honestly, your code is a mess.

One dubious part:

 data = reshape(data, M*N,1);

Does this make data matrix of just 1 column? It does not make sense.

Look at this tutorial for your own features . It has code and examples to show you what to do. See the linked web page here for more details. Matlab / Octave code can be found here .

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 @lennon310: temp=double(testimg)-m; %where 'm' is the mean of the training images L=temp'*temp; [VD]=eig(L); for i=1:size(V,2) if(D(i,i)>1) L_eig=[L_eig V(:,1)]; end end Eigenfaces=temp*L_eig;
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