一、人脸识别算法之特征脸方法（Eigenface）

1、原理介绍及数据收集

特征脸方法主要是基于PCA降维实现。

详细介绍和主要思想可以参考

http://blog.csdn.net/u010006643/article/details/46417127

上述博客的人脸数据库打不开了，大家可以去下面这个博客下载ORL人脸数据库

http://blog.csdn.net/xdzzju/article/details/50445160

下载后，ORL人脸数据库有40个人，每人10张照片。

2、流程

3、相关图示

4、代码

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  1 #include <opencv2/opencv.hpp>2 #include <opencv2/face.hpp>3 4 using namespace cv;5 using namespace cv::face;6 using namespace std;7 8 //对原图归一化9 Mat normal(Mat src, Mat dst) {10     if (src.channels() == 1)//若原图单通道11         normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);12     else //否则，原图三通道13         normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);14     return dst;15 }16 17 void main() {18     //读取文件，转换为数据流19     string filename = string("at.txt");20     ifstream file(filename.c_str(), ifstream::in);21     if (!file)22         cout << "error" << endl;23 24     string line, path, classlabel;25     vector<Mat>image;26     vector<int>labels;27     char separator = ';';28     while (getline(file,line))29     {30         stringstream liness(line);31         getline(liness, path, separator);32         getline(liness, classlabel);33         if (!path.empty()&&!classlabel.empty())34         {35             //cout << "path:" << path<< endl;36             image.push_back(imread(path, 0));37             labels.push_back(atoi(classlabel.c_str()));38         }39     }40 41     if (image.size() < 1 || labels.size() < 1)42         cout << "invalid image path..." << endl;43 44     int height = image[0].rows;45     int width = image[0].cols;46     //cout << "height:" << height << ",width:" << width<<endl;47 48     //最后一个人为测试样本49     Mat testSample = image[image.size() - 1];50     int testLabel = labels[labels.size() - 1];51     image.pop_back();52     labels.pop_back();53 54     //训练55     Ptr<BasicFaceRecognizer>model = createEigenFaceRecognizer();56     model->train(image, labels);57 58     //识别59     int predictLabel = model->predict(testSample);60     cout << "actual label:" << testLabel << ",predict label:" << predictLabel << endl;61 62     //获得特征值，特征向量，均值    平均脸63     Mat eigenvalues = model->getEigenValues();64     Mat eigenvectors = model->getEigenVectors();65     Mat mean = model->getMean();66     Mat meanFace = mean.reshape(1,height);67     Mat dst;68     dst= normal(meanFace,dst);69     imshow("Mean Face", dst);70     71     //特征脸72     for (int i = 0; i < min(10,eigenvectors.cols); i++)73     {74         Mat ev = eigenvectors.col(i).clone();75         Mat eigenFace = ev.reshape(1, height);76         Mat grayscale;77         grayscale = normal(eigenFace, grayscale);78         Mat colorface;79         applyColorMap(grayscale, colorface, COLORMAP_BONE);80         char* winTitle = new char[128];81         sprintf(winTitle, "eigenface_%d", i);82         imshow(winTitle, colorface);83     }84 85     //重建人脸86     for (int num = min(10, eigenvectors.cols); num < min(300, eigenvectors.cols); num+=15)87     {88         Mat evs = Mat(eigenvectors, Range::all(), Range(0, num));89         Mat projection = LDA::subspaceProject(evs, mean, image[0].reshape(1, 1));90         Mat reconstruction= LDA::subspaceReconstruct(evs, mean, projection);91 92         Mat result = reconstruction.reshape(1, height);93         reconstruction = normal(result, reconstruction);94         char* winTitle = new char[128];95         sprintf(winTitle, "recon_face_%d", num);96         imshow(winTitle, reconstruction);97     }98     99     waitKey(0);
100 }

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二、FisherFace（LDA线性判别分析）

1、理论介绍

http://blog.csdn.net/feirose/article/details/39552997

2、流程

3、PCA和LDA的对比

4、代码（与特征脸代码几乎一致）

此处只列出修改部分

55行模型训练 Ptr<BasicFaceRecognizer>model = createFisherFaceRecognizer();

72行显示特征脸 for (int i = 0; i < min(16,eigenvectors.cols); i++)

Mat ev = eigenvectors.col(i).clone();

86行重建人脸 for (int num = 0; num < min(16, eigenvectors.cols); num++)

三、LBPH

1、原理介绍

大家可以参考http://blog.csdn.net/xiaomaishiwoa/article/details/46640377

二、流程

3、代码

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#include <opencv2/opencv.hpp>
#include <opencv2/face.hpp>using namespace cv;
using namespace cv::face;
using namespace std;//对原图归一化
Mat normal(Mat src, Mat dst) {if (src.channels() == 1)//若原图单通道normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);else //否则，原图三通道normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);return dst;
}void main() {//读取文件，转换为数据流string filename = string("at.txt");ifstream file(filename.c_str(), ifstream::in);if (!file)cout << "error" << endl;string line, path, classlabel;vector<Mat>image;vector<int>labels;char separator = ';';while (getline(file,line)){stringstream liness(line);getline(liness, path, separator);getline(liness, classlabel);if (!path.empty()&&!classlabel.empty()){//cout << "path:" << path<< endl;image.push_back(imread(path, 0));labels.push_back(atoi(classlabel.c_str()));}}if (image.size() < 1 || labels.size() < 1)cout << "invalid image path..." << endl;int height = image[0].rows;int width = image[0].cols;//cout << "height:" << height << ",width:" << width<<endl;//最后一个人为测试样本Mat testSample = image[image.size() - 1];int testLabel = labels[labels.size() - 1];image.pop_back();labels.pop_back();//训练Ptr<LBPHFaceRecognizer>model = createLBPHFaceRecognizer();model->train(image, labels);//识别int predictLabel = model->predict(testSample);cout << "actual label:" << testLabel << ",predict label:" << predictLabel << endl;//打印参数int radius = model->getRadius();    //中心像素点到周围像素点的距离int neibs = model->getNeighbors();    //周围像素点的个数int grad_x = model->getGridX();        //将一张图片在x方向分成几块int grad_y = model->getGridY();        //将一张图片在y方向分成几块double t = model->getThreshold();    //相似度阈值    cout << "radius:" << radius << endl;cout << "neibs:" << neibs << endl;cout << "grad_x:" << grad_x << endl;cout << "grad_y:" << grad_y << endl;cout << "threshold:" << t<<endl;waitKey(0);
}

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