MATLAB: Subscripted assignment dimension mismatch.

Question

%%Simple Face Recognition Example
  %  Copyright 2014-2015 The MathWorks, Inc.
  %%Load Image Information from ATT Face Database Directory
  faceDatabase = imageSet('ATTFaces','recursive');
    %%Display Montage of First Face
    figure;
    % (faceDatabase(1) means pull out all images belong to folder 1
    montage(faceDatabase(1).ImageLocation);
    title('Images of a Single Face');

Display Query Image and Database Side-Side

    personToQuery = 1; % Call the a set of images at position 1
    galleryImage = read(faceDatabase(personToQuery),1);
    figure;
    for i=1:size(faceDatabase,2)
    imageList(i) = faceDatabase(i).ImageLocation(5);
    end
    subplot(1,2,1);
    imshow(galleryImage);
    subplot(1,2,2);
    montage(imageList);
    diff = zeros(1,9);
    %%Split Database into Training & Test Sets in the ration 80% to 20%
    [training,test] = partition(faceDatabase,[0.8 0.2]);
    %%Extract and display Histogram of Oriented Gradient Features for single face 
    person = 1;
    [hogFeature, visualization]= ...
        extractHOGFeatures(read(training(person),1));
    figure;
    subplot(2,1,1);imshow(read(training(person),1));title('Input Face');
    subplot(2,1,2);plot(visualization);title('HoG Feature');
    %%Extract HOG Features for training set 

    trainingFeatures = zeros(size(training,2)*training(1).Count,4680);
    featureCount = 1;
    for i=1:size(training,2)
        for j = 1:training(i).Count
            trainingFeatures(featureCount,:) = extractHOGFeatures(read(training(i),j));
            trainingLabel{featureCount} = training(i).Description;    
            featureCount = featureCount + 1;
        end
        personIndex{i} = training(i).Description;
    end
    %%Create 40 class classifier using fitcecoc 
    faceClassifier = fitcecoc(trainingFeatures,trainingLabel);
    %%Test Images from Test Set 
    person = 2;
    queryImage = read(test(person),1);
    queryFeatures = extractHOGFeatures(queryImage);
    personLabel = predict(faceClassifier,queryFeatures);
    % Map back to training set to find identity 
    booleanIndex = strcmp(personLabel, personIndex);
    integerIndex = find(booleanIndex);
    subplot(1,2,1);imshow(queryImage);title('Query Face');
    subplot(1,2,2);imshow(read(training(integerIndex),1));title('Matched Class');
    %%Test First 5 People from Test Set
    figure;
    figureNum = 1;
    for person=1:5
        for j = 1:test(person).Count
            queryImage = read(test(person),j);
            queryFeatures = extractHOGFeatures(queryImage);
            personLabel = predict(faceClassifier,queryFeatures);
            % Map back to training set to find identity
            booleanIndex = strcmp(personLabel, personIndex);
            integerIndex = find(booleanIndex);
            subplot(2,2,figureNum);imshow(imresize(queryImage,3));title('Query Face');
            subplot(2,2,figureNum+1);imshow(imresize(read(training(integerIndex),1),3));title('Matched Class');
            figureNum = figureNum+2;
        end
        figure;
        figureNum = 1;
end

Hello guys,

I found this tutorial on youtube that uses hog features for face recognition on MATLAB. I am playing around with it cause i am doing similar things for my dissertation. In this one when i run it as it is with the ATTFaces dataset (contains 10 images for each individual, 40 in total in .pgm format) it works fine. When i try to change the dataset, using the YALE faces, or a custom dataset i have my personal images i get this mismatch error that you can see in the title. The other datasets have images in .jpg by the way and are more that 10 per folder. Can someone help me and tell me why i get this error? I gave you information about the format of the images and thier size. Also the number of them in the folders. Maybe one of these parameters makes it fail. If yes, which one.

Regards to everyone 🙂

EDIT*** I 've googled the error first obviously but i wasn't able to find the reason why is happening. I suspect one of the things i asked in the end… Yea i forgot to mention the error with different datasets occurs here:

%%Extract HOG Features for training set 
    trainingFeatures = zeros(size(training,2)*training(1).Count,4680);

and it shows the error you see on the title.

The code as you it, it works using the AT&T dataset. IF i change it to a different dataset shows that error i mentioned in that particular line.

Answer 1

.jpg files are almost always RGB files, even if they look gray. Code that is expecting grayscale images will often fail on RGB files.