用matlab写一个实现人脸识别的代码

这里提供一个基于MATLAB的人脸识别的简单示例代码，使用了Eigenface方法：

% 读取人脸图片
faceDatabase = imageSet('人脸图片路径');

% 生成训练集和测试集
[trainingSet, testSet] = partition(faceDatabase, [0.8 0.2]);

% 训练Eigenface模型
trainingFeatures = zeros(size(trainingSet,2)*trainingSet(1).Count, numel(trainingSet(1).ImageLocation));
featureCount = 1;
for i=1:size(trainingSet,2)
    for j=1:trainingSet(i).Count
        img = read(trainingSet(i),j);
        trainingFeatures(:,featureCount) = img(:);
        featureCount = featureCount + 1;
    end
end
averageFace = mean(trainingFeatures, 2);
trainingFeatures = trainingFeatures - repmat(averageFace, 1, size(trainingFeatures,2));
[eigenvectors, eigenvalues] = eig(trainingFeatures'*trainingFeatures);
eigenfaces = trainingFeatures * eigenvectors;
eigenfaces = eigenfaces ./ repmat(sqrt(sum(eigenfaces.^2)), size(eigenfaces,1), 1);

% 测试Eigenface模型
trueLabels = zeros(1, numel(testSet));
predictedLabels = zeros(1, numel(testSet));
confidenceScores = zeros(1, numel(testSet));
testFeatures = zeros(size(trainingFeatures,1), numel(testSet));
featureCount = 1;
for i=1:size(testSet,2)
    for j=1:testSet(i).Count
        img = read(testSet(i),j);
        testFeatures(:,featureCount) = img(:) - averageFace;
        featureCount = featureCount + 1;
        trueLabels(featureCount) = i;
    end
end
for i=1:numel(testSet)
    testCoeffs = eigenfaces' * testFeatures(:,i);
    distances = zeros(1, size(trainingFeatures,2));
    for j=1:size(trainingFeatures,2)
        coeffDiff = testCoeffs - eigenfaces' * trainingFeatures(:,j);
        distances(j) = norm(coeffDiff);
    end
    [minDistance, index] = min(distances);
    predictedLabels(i) = index;
    confidenceScores(i) = 1 - minDistance / norm(testCoeffs);
end

% 显示结果
figure;
for i=1:numel(testSet)
    subplot(5,5,i);
    imshow(read(testSet(i),1));
    title(sprintf('预测： %s\n置信度： %f', trainingSet(predictedLabels(i)).Description, confidenceScores(i)));
end

注意：这只是一个简单的示例代码，实际使用中需要根据具体的数据和应用场景进行调整和优化。