基于OpenCV的EigenFace FisherFace LBPHFace人脸识别的实现

OpenCV自带了丰富的人脸识别库，本文通过阅读OpenCV文档，实现了人脸识别的三种经典算法：PCA（特征脸方法），LDA（线性判别分析），以及LBP（Local Binary Patterns，局部二值模式）方法。人脸数据集采用的是Yale的人脸数据库和att_faces人脸库，下载链接http://pan.baidu.com/s/1hrmZRZe 
下面附上代码：

/*2015.12.31，by xdzzju*//*改程序的功能是利用opencv的人脸识别库，训练及测试yale人脸库和att_faces人脸库，可选用三种训练模型，eigenface，fisherface及LBP*//*编程环境为vs2012+opencv2.4.9*/#include "iostream"#include "stdlib.h"#include "vector"#include "opencv2/core/core.hpp"#include "opencv2/contrib/contrib.hpp"#include "opencv2/highgui/highgui.hpp"using namespace std;using namespace cv;int main(){    vector<Mat> images;    vector<int> labels;    cout<<"choose dataset:"<<endl;    int datasetChoose;    cin>>datasetChoose;                                 //选择数据集    if(datasetChoose==1)    {        string* fileName=new string[166];        for(int i=1;i<=165;i++)                         //将文件名编号放入string数组，从1开始        {            char* temp=new char[3];            itoa(i,temp,10);            fileName[i]="yaleFaceDataset/s";            fileName[i]+=temp;            fileName[i]+=".bmp";        }        // load images        for(int i=1;i<=165;i++)        {            if(i%11)                                    //将每组人脸的前十张用于训练，最后一张用于测试            {                images.push_back(imread(fileName[i], CV_LOAD_IMAGE_GRAYSCALE));                labels.push_back(i/11+1);            }        }        int modelChoose;        Ptr<FaceRecognizer> model;        while(1)        {            cout<<"choose model:"<<endl;            cout<<"1 EigenFace"<<endl;            cout<<"2 Fisher"<<endl;            cout<<"3 LBPH"<<endl;            cin>>modelChoose;            switch(modelChoose)            {            case 1:model =  createEigenFaceRecognizer();break;            case 2:model =  createFisherFaceRecognizer();break;            case 3:model =  createLBPHFaceRecognizer();break;            default:model =  createLBPHFaceRecognizer();break;            }            model->train(images,labels);            for(int i=11;i<=165;i+=11)            {                Mat image=imread(fileName[i], CV_LOAD_IMAGE_GRAYSCALE);                int predict=model->predict(image);                cout<<predict<<endl;            }        }    }    else if(datasetChoose==2)    {        int predictRight=0,predictWrong=0;        string fileName;        char* tempdir=new char[3];        char* tempFilename=new char[3];        for(int i=1;i<=40;i++)                      //导入图片，每组前9张作为训练集，最后一张用来测试        {            for(int j=1;j<=9;j++)            {                fileName="att_faces/";                itoa(i,tempdir,10);                fileName+="s";                fileName+=tempdir;                fileName+="/";                itoa(j,tempFilename,10);                fileName+=tempFilename;                fileName+=".bmp";                Mat tempImage=imread(fileName, CV_LOAD_IMAGE_GRAYSCALE);                images.push_back(tempImage);                labels.push_back(i);            }        }        int modelChoose;        Ptr<FaceRecognizer> model;        while(1)        {            predictRight=0;            predictWrong=0;            model.release();            cout<<"choose model:"<<endl;            cout<<"1 EigenFace"<<endl;            cout<<"2 Fisher"<<endl;            cout<<"3 LBPH"<<endl;            cin>>modelChoose;            switch(modelChoose)            {            case 1:model =  createEigenFaceRecognizer();break;            case 2:model =  createFisherFaceRecognizer();break;            case 3:model =  createLBPHFaceRecognizer();break;            default:model =  createLBPHFaceRecognizer();break;            }            model->train(images,labels);            for(int i=1;i<=40;i++)            {                fileName="att_faces/";                itoa(i,tempdir,10);                fileName+="s";                fileName+=tempdir;                fileName+="/";                fileName+="10.bmp";                cout<<fileName<<endl;                Mat image=imread(fileName, CV_LOAD_IMAGE_GRAYSCALE);                int predict=model->predict(image);                if(predict==i)predictRight++;                else predictWrong++;                cout<<predict<<endl;            }            cout<<"right:"<<predictRight<<endl;            cout<<"wrong:"<<predictWrong<<endl;        }    }    return 0;}