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% Step 1: Train the model

% Load pre-trained MobileNetV2
net = mobilenetv2;

% Specify the folders containing the images for each face
face1_folder = 'Face1Images';
face2_folder = 'Face2Images';

% Create an imageDatastore for each face
imds_face1 = imageDatastore(face1_folder, 'LabelSource', 'foldernames');
imds_face2 = imageDatastore(face2_folder, 'LabelSource', 'foldernames');

% Preprocess images for MobileNetV2
inputSize = net.Layers(1).InputSize;
augmentedTrainingData_face1 = augmentedImageDatastore(inputSize(1:2), imds_face1);
augmentedTrainingData_face2 = augmentedImageDatastore(inputSize(1:2), imds_face2);

% Replace the last layer of MobileNetV2 for transfer learning
numClasses = 2; % Two classes: face1 and face2
layers = [
    net.Layers(1:end-3)
    fullyConnectedLayer(numClasses, 'Name', 'fc')
    softmaxLayer('Name', 'softmax')
    classificationLayer('Name', 'classoutput')];

% Set options for transfer learning
options = trainingOptions('adam', ...
    'InitialLearnRate', 0.001, ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 32, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', true, ...
    'Plots', 'training-progress');

% Fine-tune MobileNetV2 on the dataset
net_finetuned = trainNetwork(augmentedTrainingData_face1, layers, options);

% Save the fine-tuned model
save('finetuned_mobilenetv2.mat', 'net_finetuned', '-v7.3');

disp('Model training complete.');
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