I am trying to implement a feature extractor using gabor filter in MATLAB. I have taken images from the JAFFE Database. Here is my code:

angry_database = dir('Path_for_directory');
content_length = length(angry_database);

angry_feat = [];
for ii = 3:content_length
    current_file_name = angry_database(ii).name;
    %reads the image from the database
    im = imread(current_file_name);
    %detects the face from the image
    FDetect = vision.CascadeObjectDetector;
    x = step(FDetect, im);
    face = imcrop(im, x); %creates the entire new image after clever cropping using the algo
    %detects the eye from the face using viola-jones algorithm
    EyeDetect = vision.CascadeObjectDetector('EyePairBig');
    BBE = step(EyeDetect, im);
    [x y z] = size(BBE);
    eyes = imcrop(im, BBE(x,:,:));
    gaborArray = gabor([4 8 12 16 20], [0 30 45 60 75 90 105 120]);
    %forms the feature Array of the face
    gaborFiltFace = imgaborfilt(face, gaborArray);
    meanArrFace = zeros(1, 40);
    stdArrFace = zeros(1, 40);
    %forms the feature array for the eyes
    gaborFiltEyes = imgaborfilt(eyes, gaborArray);
    meanArrEyes = zeros(1, 40);
    stdArrEyes = zeros(1, 40);
    for i=1:40
        meanArrFace(i) = mean2(gaborFiltFace(:,:,i));
        stdArrFace(i) = std2(gaborFiltFace(:,:,i));
        meanArrEyes(i) = mean2(gaborFiltEyes(:,:,i));
        stdArrEyes(i) = std2(gaborFiltEyes(:,:,i));
    end
    featureArray = (horzcat(meanArrFace, stdArrFace, meanArrEyes, stdArrEyes));
    featureArray = featureArray./ max(featureArray);
    featureArray = [num2cell(featureArray) 'A']; 
    angry_feat = [angry_feat; featureArray];
end

The above code is the MATLAB code, it generates a feature vector which is 1x161 matrix, I store this in a csv file and subsequently do for the rest of the images. However, when I implement the csv file in the classifier of WEKA training machine, the classifier shows poor results, the error is close to 90% (worse). I have normalised the data, even used the viola jones, but the error reduction is very small. The above code is the final code that i tried before seeking help. As I am pretty new to MATLAB, can you please tell where I am going wrong, and please suggest me what should I do, so that I can get my desired results?

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