编写用K-L变换法进行人脸识别的程序,对标准图像库进行人脸识别实验。ORL_faces图像库中有40组人脸图像,每组内的10幅图像取自同一个人,部分人脸图像如下图所示。利用每组中的前5幅人脸图像(共200幅)作为训练样本,生成人脸识别的分类器,显示“平均脸”图像和“特征脸”图像。用剩下的200幅图像作为测试样本,输出识别结果,检测识别率。给出matlab完整代码
时间: 2024-03-24 11:39:06 浏览: 12
以下是使用 MATLAB 实现 K-L 变换人脸识别的完整代码。其中,我们使用了 eigenface 函数来实现 K-L 变换的计算。在运行代码之前,请确保已经下载 ORL_faces 数据集,并将其放在当前 MATLAB 工作目录下的 orl_faces 文件夹中。
```matlab
% 加载数据集
datasetPath = 'orl_faces';
imds = imageDatastore(datasetPath,'IncludeSubfolders',true,'LabelSource','foldernames');
% 将每个人的前5张图像作为训练集,后5张作为测试集
[trainSet, testSet] = splitEachLabel(imds, 5, 'randomized');
% 计算训练集的平均脸和特征脸
trainImgs = readall(trainSet);
trainMat = reshape(cell2mat(trainImgs), [size(trainImgs{1}), numel(trainImgs)]);
avgFace = mean(trainMat, 3);
flattenedFaces = reshape(trainMat, size(trainMat, 1) * size(trainMat, 2), size(trainMat, 3));
meanSubtracted = flattenedFaces - avgFace(:);
[~, scores, ~] = pca(meanSubtracted');
eigenFaces = scores(:, 1:25);
% 显示平均脸和特征脸
figure;
subplot(4, 7, 1);
imshow(avgFace, []);
title('平均脸');
for i = 1:25
subplot(4, 7, i+1);
imshow(reshape(eigenFaces(:,i), size(trainMat, 1), size(trainMat, 2)), []);
title(sprintf('特征脸 %d', i));
end
% 对测试集进行识别
testImgs = readall(testSet);
testMat = reshape(cell2mat(testImgs), [size(testImgs{1}), numel(testImgs)]);
flattenedTestFaces = reshape(testMat, size(testMat, 1) * size(testMat, 2), size(testMat, 3));
meanSubtractedTest = flattenedTestFaces - avgFace(:);
testScores = meanSubtractedTest' * eigenFaces;
predictedLabels = zeros(length(testSet.Files), 1);
for i = 1:length(testSet.Files)
dists = sum((testScores(i,:) - scores(:,1:25)).^2, 2);
[~, idx] = min(dists);
predictedLabels(i) = ceil(idx / 5);
end
% 输出识别率
actualLabels = str2double(testSet.Labels);
accuracy = sum(predictedLabels == actualLabels) / length(actualLabels);
fprintf('识别率为 %.2f%%\n', accuracy * 100);
```
运行代码后,程序将会输出识别率,并显示出平均脸和前 25 个特征脸。