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;
}

来源：https://blog.csdn.net/qq_24946843/article/details/82876629