matlab人脸识别代码RBG

以下是使用 MATLAB 进行 RGB 人脸识别的示例代码：

首先，需要加载训练样本和测试图像。训练样本应该包含多个人的 RGB 图像，测试图像应该是要进行识别的图像。

% Load training images
faceDatabase = imageSet('path/to/training/images');

% Load test image
testImage = imread('path/to/test/image');

接下来，将训练样本转换为灰度图像，并提取每个图像的特征向量。这里使用主成分分析 (PCA) 进行特征提取。

% Convert training images to grayscale
for i = 1:faceDatabase.Count
    img = read(faceDatabase, i);
    imgGray = rgb2gray(img);
    trainingData(:,i) = imgGray(:);
end

% Perform principal component analysis (PCA) on the training data
[coeff, score, latent] = pca(trainingData');

然后，将测试图像转换为灰度图像，并将其投影到主成分空间中。这里只使用前 N 个主成分。

% Convert test image to grayscale
testImageGray = rgb2gray(testImage);

% Project test image onto principal component space
numComponents = 100;
testData = double(testImageGray(:));
testDataCentered = testData - mean(trainingData, 2);
testDataProjected = coeff(:,1:numComponents)' * testDataCentered;

最后，比较测试图像的投影向量与训练样本的投影向量，并找到最接近的匹配。这里使用欧几里得距离进行匹配。

% Compare test image to training images using Euclidean distance
minDistance = Inf;
minIndex = -1;
for i = 1:faceDatabase.Count
    dist = norm(testDataProjected - score(i,1:numComponents)');
    if dist < minDistance
        minDistance = dist;
        minIndex = i;
    end
end

% Display result
match = read(faceDatabase, minIndex);
imshow(match);

完整代码如下：

% Load training images
faceDatabase = imageSet('path/to/training/images');

% Load test image
testImage = imread('path/to/test/image');

% Convert training images to grayscale
for i = 1:faceDatabase.Count
    img = read(faceDatabase, i);
    imgGray = rgb2gray(img);
    trainingData(:,i) = imgGray(:);
end

% Perform principal component analysis (PCA) on the training data
[coeff, score, latent] = pca(trainingData');

% Convert test image to grayscale
testImageGray = rgb2gray(testImage);

% Project test image onto principal component space
numComponents = 100;
testData = double(testImageGray(:));
testDataCentered = testData - mean(trainingData, 2);
testDataProjected = coeff(:,1:numComponents)' * testDataCentered;

% Compare test image to training images using Euclidean distance
minDistance = Inf;
minIndex = -1;
for i = 1:faceDatabase.Count
    dist = norm(testDataProjected - score(i,1:numComponents)');
    if dist < minDistance
        minDistance = dist;
        minIndex = i;
    end
end

% Display result
match = read(faceDatabase, minIndex);
imshow(match);