Opencv EigenFace人脸识别算法详解

简要：

EigenFace是基于PCA降维的人脸识别算法，PCA是使整体数据降维后的方差最大，没有考虑降维后类间的变化。 它是将图像每一个像素当作一维特征，然后用SVM或其它机器学习算法进行训练。但这样维数太多，根本无法计算。我这里用的是ORL人脸数据库，英国剑桥实验室拍摄的，有40位志愿者的人脸，在不同表情不同光照下每位志愿者拍摄10张，共有400张图片，大小为112*92，所以如果把每个像素当做特征拿来训练的话，一张人脸就有10304维特征，这么高维的数据根本无法处理。所以需要先对数据进行降维，去掉一些冗余的特征。

第一步：将ORL人脸图片的地址统一放在一个文件里，等会通过对该文件操作，将图片全部加载进来。

//ofstream一般对文件进行读写操作，ifstream一般对文件进行读操作
ofstream file;
 file.open("path.txt");//新建并打开文件
 char str[50] = {};
 for (int i = 1; i <= 40; i++) {
 for (int j = 1; j <= 10; j++) { 
  sprintf_s(str, "orl_faces/s%d/%d.pgm;%d", i, j, i);//将数字转换成字符
  file << str << endl;//写入
 } 
 }

得到路劲文件如下图所示：

 第二步：读入模型需要输入的数据，即用来训练的图像vector<Mat>images和标签vector<int>labels

string filename = string("path.txt");
 ifstream file(filename);
 if (!file) { 
    printf("could not load file"); 
  }
 vector<Mat>images;
 vector<int>labels;
 char separator = ';';
 string line,path, classlabel;
 while (getline(file,line)) {
 stringstream lines(line);
 getline(lines, path, separator);
 getline(lines, classlabel);
 images.push_back(imread(path, 0));
 labels.push_back(atoi(classlabel.c_str()));//atoi(ASCLL to int)将字符串转换为整数型
 }

第三步：加载、训练、预测模型

Ptr<BasicFaceRecognizer> model = EigenFaceRecognizer::create();
 model->train(images, labels);
 int predictedLabel = model->predict(testSample);
 printf("actual label:%d,predict label :%d\n", testLabel, predictedLabel);

补充：

1、显示平均脸

//计算特征值特征向量及平均值
 Mat vals = model->getEigenValues();//89*1
 printf("%d,%d\n", vals.rows, vals.cols);
 Mat vecs = model->getEigenVectors();//10324*89
 printf("%d,%d\n", vecs.rows, vecs.cols);
 Mat mean = model->getMean();//1*10304
 printf("%d,%d\n", mean.rows, mean.cols);
 
 //显示平均脸
 Mat meanFace = mean.reshape(1, height);//第一个参数为通道数，第二个参数为多少行
 normalize(meanFace, meanFace, 0, 255, NORM_MINMAX, CV_8UC1);
 imshow("Mean Face", meanFace);

2、显示前部分特征脸

//显示特征脸
 for (int i = 0; i<min(10, vals.rows); i++) {
 Mat feature_vec = vecs.col(i).clone();
 Mat feature_face= feature_vec.reshape(1, height); 
 normalize(feature_face, feature_face, 0, 255, NORM_MINMAX, CV_8UC1); 
 Mat colorface;
 applyColorMap(feature_face, colorface, COLORMAP_BONE);
 
 sprintf_s(win_title, "eigenface%d", i);
 imshow(win_title, colorface);
 }

3、对第一张人脸在特征向量空间进行人脸重建（分别基于前10,20,30,40,50,60个特征向量进行人脸重建）

//重建人脸
 for (int i = min(10, vals.rows); i <min(61, vals.rows); i+=10) {
 Mat vecs_space = Mat(vecs, Range::all(), Range(0, i));
 Mat projection = LDA::subspaceProject(vecs_space, mean, images[0].reshape(1, 1));//投影到子空间
 Mat reconstruction = LDA::subspaceReconstruct(vecs_space, mean, projection);//重建
 Mat result = reconstruction.reshape(1, height);
 normalize(result, result, 0, 255, NORM_MINMAX, CV_8UC1);
 //char wintitle[40] = {};
 sprintf_s(win_title, "recon face %d", i);
 imshow(win_title, result);
 }

完整代码如下：

#include<opencv2\opencv.hpp>
#include<opencv2\face.hpp>
using namespace cv;
using namespace face;
using namespace std;
char win_title[40] = {};
 
int main(int arc, char** argv) { 
 namedWindow("input",CV_WINDOW_AUTOSIZE);
 
 //读入模型需要输入的数据，用来训练的图像vector<Mat>images和标签vector<int>labels
 string filename = string("path.txt");
 ifstream file(filename);
 if (!file) { printf("could not load file"); }
 vector<Mat>images;
 vector<int>labels;
 char separator = ';';
 string line,path, classlabel;
 while (getline(file,line)) {
 stringstream lines(line);
 getline(lines, path, separator);
 getline(lines, classlabel);
 //printf("%d\n", atoi(classlabel.c_str()));
 images.push_back(imread(path, 0));
 labels.push_back(atoi(classlabel.c_str()));//atoi(ASCLL to int)将字符串转换为整数型
 }
 int height = images[0].rows;
 int width = images[0].cols;
 printf("height:%d,width:%d\n", height, width);
 //将最后一个样本作为测试样本
 Mat testSample = images[images.size() - 1];
 int testLabel = labels[labels.size() - 1];
 //删除列表末尾的元素
 images.pop_back();
 labels.pop_back();
 
 //加载，训练，预测
 Ptr<BasicFaceRecognizer> model = EigenFaceRecognizer::create();
 model->train(images, labels);
 int predictedLabel = model->predict(testSample);
 printf("actual label:%d,predict label :%d\n", testLabel, predictedLabel);
 
 //计算特征值特征向量及平均值
 Mat vals = model->getEigenValues();//89*1
 printf("%d,%d\n", vals.rows, vals.cols);
 Mat vecs = model->getEigenVectors();//10324*89
 printf("%d,%d\n", vecs.rows, vecs.cols);
 Mat mean = model->getMean();//1*10304
 printf("%d,%d\n", mean.rows, mean.cols);
 
 //显示平均脸
 Mat meanFace = mean.reshape(1, height);//第一个参数为通道数，第二个参数为多少行
 normalize(meanFace, meanFace, 0, 255, NORM_MINMAX, CV_8UC1);
 imshow("Mean Face", meanFace);
 
 //显示特征脸
 for (int i = 0; i<min(10, vals.rows); i++) {
 Mat feature_vec = vecs.col(i).clone();
 Mat feature_face= feature_vec.reshape(1, height); 
 normalize(feature_face, feature_face, 0, 255, NORM_MINMAX, CV_8UC1); 
 Mat colorface;
 applyColorMap(feature_face, colorface, COLORMAP_BONE);
 
 sprintf_s(win_title, "eigenface%d", i);
 imshow(win_title, colorface);
 }
 
 //重建人脸
 for (int i = min(10, vals.rows); i <min(61, vals.rows); i+=10) {
 Mat vecs_space = Mat(vecs, Range::all(), Range(0, i));
 Mat projection = LDA::subspaceProject(vecs_space, mean, images[0].reshape(1, 1));
 Mat reconstruction = LDA::subspaceReconstruct(vecs_space, mean, projection);
 Mat result = reconstruction.reshape(1, height);
 normalize(result, result, 0, 255, NORM_MINMAX, CV_8UC1);
 //char wintitle[40] = {};
 sprintf_s(win_title, "recon face %d", i);
 imshow(win_title, result);
 }
 
 waitKey(0);
 return 0;
}

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