SVM训练3D活体识别模型

训练数据采集分为正样本和负样本

正样本：正脸、侧脸、抬头、低头、表情，4000张

负样本：平面照片、弯曲照片、褶皱照片、照片抠眼鼻嘴做简单面具、电子屏，4000张

分辨率为128*128，因为1米以内的人脸框大小在100多左右

保存格式为.xml

FileStorage fs(savename, FileStorage::WRITE);//savename表示保存的文件名
fs << "depth" << image2;//image2表示128*128的16位深度人脸框信息
cout << savename << " ,save successful" << endl;
fs.release();

然后开始训练模型

 

LBP.h
#ifndef __LBP_H__
#define __LBP_H__
#include "opencv2/opencv.hpp"
#include<vector>
using namespace std;
using namespace cv;

class LBP
{
public:
	void elbp(const Mat &src, Mat &dst, int radius, int neighbors);
};

#endif

 

 

//SVMTest.h

#ifndef __SVMTEST__
#define __SVMTEST__

#include "opencv2/ml.hpp"
//#include"../Utility/CommonUtility.h"
//#include"../Utility/LogInterface.h"
#include<fstream>
#include"LBP.h"
using namespace cv::ml;

// if you do not need log,comment it,just like :#define LOG_WARN_SVM_TEST(...)                  //LOG4CPLUS_MACRO_FMT_BODY ("SVMTest", WARN_LOG_LEVEL, __VA_ARGS__)
#define LOG_DEBUG_SVM_TEST(...)                //LOG4CPLUS_MACRO_FMT_BODY ("SVMTest", DEBUG_LOG_LEVEL, __VA_ARGS__)
#define LOG_ERROR_SVM_TEST(...)                 //LOG4CPLUS_MACRO_FMT_BODY ("SVMTest", ERROR_LOG_LEVEL, __VA_ARGS__)
#define LOG_INFO_SVM_TEST(...)                    //LOG4CPLUS_MACRO_FMT_BODY ("SVMTest", INFO_LOG_LEVEL, __VA_ARGS__)
#define LOG_WARN_SVM_TEST(...)                  //LOG4CPLUS_MACRO_FMT_BODY ("SVMTest", WARN_LOG_LEVEL, __VA_ARGS__)
//#define CONFIG_FILE                                              "./Resource/Configuration.xml"
#define CELL_SIZE   16

class SVMTest
{
public:
	SVMTest(const string &_trainDataFileList,
		const string &_testDataFileList,
		const string &_svmModelFilePath,
		const string &_predictResultFilePath,
		SVM::Types svmType, // See SVM::Types. Default value is SVM::C_SVC.
		SVM::KernelTypes kernel,
		double c, // For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.
		double coef,  // For SVM::POLY or SVM::SIGMOID. Default value is 0.
		double degree, // For SVM::POLY. Default value is 0.
		double gamma, // For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.
		double nu,  // For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.
		double p // For SVM::EPS_SVR. Default value is 0.
	);
	bool Initialize();
	virtual ~SVMTest();

	void Train();
	void Predict();

private:
	string trainDataFileList;
	string testDataFileList;
	string svmModelFilePath;
	string predictResultFilePath;
	string predictResultError;

	// SVM
	Ptr<SVM> svm;

	// feature extracting(HOG,LBP,Haar,etc)
	LBP lbp;


};


#endif // SVMTEST

SVM训练模型主要参考了http://blog.youkuaiyun.com/qianqing13579，自己改了些东西，先表示感谢！

//LBP.cpp
#include"LBP.h"
void elbp(const Mat &src, Mat &dst, int radius, int neighbors) 
{
	CV_As