facial recognition in matlab

main.m
% Load the dataset
dataset = imageSet('path-to-dataset folder');
% Resize images to be the same size
imageSize = [200, 200];
inputImages = cell(1, dataset.Count);
for i=1:dataset.Count
    img = read(dataset, i);
    img = imresize(img, imageSize);
    inputImages{i} = rgb2gray(img);
end
% Create a matrix to store the flattened images
X = zeros(prod(imageSize), length(inputImages));
for i=1:length(inputImages)
    X(:,i) = inputImages{i}(:);
end
% Compute the average face
averageFace = mean(X,2);
% Subtract the mean from each image
X = X - repmat(averageFace, 1, length(inputImages));
% Compute the eigenvectors of the covariance matrix
[V, ~] = eig(X'*X);
% Take the largest eigenvectors as the principal components
numComponents = 50;
eigenvectors = X*V(:, end:-1:end-numComponents+1);
% Project each image onto the principal components
features = eigenvectors'*X;
% Test an unknown image
unknown = imread('path-to-unknown-image');
unknown = imresize(unknown, imageSize);
unknown = rgb2gray(unknown);
unknownFeatures = eigenvectors'*(unknown(:)-averageFace);
% Compute the Euclidean distance to each known image
distances = zeros(1, dataset.Count);
for i=1:dataset.Count
    distances(i) = norm(features(:,i)-unknownFeatures);
end
% Find the closest match
[~,index] = min(distances);
% Display the result
figure;
subplot(1,2,1); imshow(unknown); title('Unknown Image','FontSize',14);
subplot(1,2,2); imshow(read(dataset,index)); title('Matched Image','FontSize',14);
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