用matlab代码实现人脸识别系统设计-关于性别的人脸识别系统设计,特征提取算法 FLDA,分类器Bayes分类器。

以下是一个使用MATLAB实现基于FLDA和Bayes分类器的性别人脸识别系统的示例代码：

%% 数据导入
data = csvread('gender_data.csv');
labels = data(:,1);
features = data(:,2:end);

%% 数据划分
[trainInd,testInd] = dividerand(length(labels),0.8,0.2);

trainFeatures = features(trainInd,:);
trainLabels = labels(trainInd,:);
testFeatures = features(testInd,:);
testLabels = labels(testInd,:);

%% 特征提取
numComponents = 10; % 选择投影的维度
model = fisherfaces(trainFeatures, trainLabels, numComponents);

% 将训练数据和测试数据投影到FLD空间中
trainFeaturesFLD = projectImages(trainFeatures, model.W);
testFeaturesFLD = projectImages(testFeatures, model.W);

%% 分类器
% 计算每个类别的先验概率
priorMale = sum(trainLabels == 0) / length(trainLabels);
priorFemale = sum(trainLabels == 1) / length(trainLabels);

% 计算每个类别在FLD空间中的均值和协方差矩阵
maleFLD = trainFeaturesFLD(trainLabels == 0,:);
femaleFLD = trainFeaturesFLD(trainLabels == 1,:);
meanMaleFLD = mean(maleFLD);
meanFemaleFLD = mean(femaleFLD);
covMaleFLD = cov(maleFLD);
covFemaleFLD = cov(femaleFLD);

% 对测试数据进行分类
predictedLabels = zeros(size(testLabels));
for i = 1:length(testLabels)
testFLD = testFeaturesFLD(i,:);

% 计算每个类别的条件概率
likelihoodMale = mvnpdf(testFLD, meanMaleFLD, covMaleFLD);
likelihoodFemale = mvnpdf(testFLD, meanFemaleFLD, covFemaleFLD);

% 判断测试数据属于哪个类别
posteriorMale = likelihoodMale * priorMale;
posteriorFemale = likelihoodFemale * priorFemale;
if posteriorMale > posteriorFemale
predictedLabels(i) = 0;
else
predictedLabels(i) = 1;
end
end

%% 评估系统性能
accuracy = sum(predictedLabels == testLabels) / length(testLabels);
precisionMale = sum(predictedLabels(testLabels == 0) == 0) / sum(testLabels == 0);
precisionFemale = sum(predictedLabels(testLabels == 1) == 1) / sum(testLabels == 1);
recallMale = sum(predictedLabels(testLabels == 0) == 0) / length(testLabels(testLabels == 0));
recallFemale = sum(predictedLabels(testLabels == 1) == 1) / length(testLabels(testLabels == 1));

disp(['Accuracy: ', num2str(accuracy)]);
disp(['Precision (male): ', num2str(precisionMale)]);
disp(['Precision (female): ', num2str(precisionFemale)]);
disp(['Recall (male): ', num2str(recallMale)]);
disp(['Recall (female): ', num2str(recallFemale)]);

需要注意的是，这只是一个简单的示例代码，实际应用中可能需要针对不同的数据集进行参数调整和优化。