描写叙述

人脸识别包含四个步骤

1. 人脸检測：定位人脸区域，仅仅关心是不是脸；
2. 人脸预处理：对人脸检測出来的图片进行调整优化。
3. 收集和学习人脸：收集要识别的人的预处理过的人脸，然后通过一些算法去学习怎样识别；
4. 人脸识别：识别当前人脸与数据库里的哪个人脸最类似。

人脸检測

OpenCV集成了基于PCA LDA 和LBP的人脸检測器。源文件自带非常多各种训练好的检測器。下表是经常使用的XML文件

上面的XML文件能够检測正面人脸、眼睛或鼻子。检測人脸我採用的是第一个或第二个Harr人脸检測器。

识别率比較好。

第一步：载入Harr人脸检測XML文件

try{faceCascade.load(faceCascadeFilename);}catch(cv::Exception& e){}if ( faceCascade.empty() ) {cerr << "ERROR: Could not load Face Detection cascade classifier [" << faceCascadeFilename << "]!" << endl;cerr << "Copy the file from your OpenCV data folder (eg: 'C:\\OpenCV\\data\\haarcascade_frontalface_alt2') into this WebcamFaceRec folder." << endl;exit(1);}cout << "Loaded the Face Detection cascade classifier [" << faceCascadeFilename << "]." << endl;

第二步：载入摄像头，从视频获取图像帧。

try{videoCapture.open(CameraID);}catch(cv::Exception& e){}if(!videoCapture.isOpened()){cerr << "ERROR: could not open Camera!" << endl;exit(1);}videoCapture >> cameraFrame;

第三步：一帧图像预处理

1、 灰度转换：使用cvtColor()函数，将彩色图像转换为灰度图像。台式机是3通道的BGR。移动设备则是4通道的BGRA格式

if(srcimg.channels() ==3 ){cvtColor(srcimg,gray_img,CV_BGR2GRAY);}else if(srcimg.channels() ==4 ){cvtColor(srcimg,gray_img,CV_BGRA2GRAY);}else {gray_img = srcimg;}

2、直方图均衡化，在OpenCV函数中利用equalizeHist()函数运行直方图均衡化，提升对照度和亮度。

equalizeHist(gray_img,equalized_Img);

第四步：检測人脸

上面已经创建了级联分类器并载入好XML文件。接着使用函数Classifier::detecMultiScale()函数来检測人脸。这个函数的參数说明：
a、minFeatureSize: 该參数决定最小的人脸大小。通常能够设为20*20或30*30像素。假设使用摄像机或移动设备检測，则人脸一般非常接近摄像机，可把參数调大。80*80；
b、searchScaleFactor: 该參数决定有多少不同大小的人脸要搜索，通常设为1.1
c、minNeighbors： 该參数决定检測器怎样确定人脸已经被检測到。通常设为3
d、flags: 该參数设定是否要查找全部的人脸或最大的人脸
（CASCADE_FIND_BIGGEST_OBJECT）

int flags = CASCADE_FIND_BIGGEST_OBJECT;//smallest object SizeSize minFeatureSize = Size(20,20);// How detailed should the search be. Must be larger than 1.0.float searchScaleFactor = 1.1f;// How much the detections should be filtered out. This should depend on how bad false detections are to your system.// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.int minNeighbors = 6;vector<Rect> faces;faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,minNeighbors,flags,minFeatureSize);//faceCascade.detectMultiScale(equalized_Img, faces);int i = 0;for(i = 0; i < faces.size(); i++){Rect face_id = faces[i];rectangle(orginalimg,face_id,Scalar(0,255,0),1);}

人脸识别

为了识别人脸。须要收集足够多的要识别的人的人脸图像。

收集好之后，选择适合人脸识别的机器学习算法。通过算法来学习收集的数据。从而训练出一个模型并保存。下次进来一帧图像，通过算法对模型里的參数进行匹配识别。人脸识别机器学习算法有非常多，如SVM（支持向量机），ANN（人工神经网络）还有最经常使用的是基于特征脸的算法。OpenCV提供了CV::Algorithm类，类中有基于特征脸的（PCA 主成分分析）、Fisher脸（LDA 线性判别分析）和LPBH（局部二值模式直方图）

使用里面的算法，第一步必须通过cv::Algorithm::creat< FaceRecognizer>创建一个FaceRecognizer对象。创建了FaceRecognizer对象之后。将收集的人脸数据和标签传递给FaceRecognizer::train() 函数就可以进行训练模型。

string facerecAlgorithm = "FaceRecognizer.Fisherfaces";
Ptr<FaceRecognizer> model;
// Use OpenCV's new FaceRecognizer in the "contrib" module:
model = Algorithm::create<FaceRecognizer>(facerecAlgorithm);
if (model.empty()) {
cerr << "ERROR: The FaceRecognizer [" << facerecAlgorithm;cerr << "] is not available in your version of OpenCV. ";cerr << "Please update to OpenCV v2.4.1 or newer." << endl;exit(1);
}model->train(preprocessedFaces, faceLabels);

训练好模型之后。通常是把模型保存下来，以免下次反复训练。

直接载入模型就可以。下一步就是人脸识别。相同，opencv把识别算法集成在FaceRecognizer类中。简单地调用FaceRecognizer::predict() 就能够识别。

int identity = model->predict(preprocessedFace);

測试程序

/** Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.* Released to public domain under terms of the BSD Simplified license.** Redistribution and use in source and binary forms, with or without* modification, are permitted provided that the following conditions are met:*   * Redistributions of source code must retain the above copyright*     notice, this list of conditions and the following disclaimer.*   * Redistributions in binary form must reproduce the above copyright*     notice, this list of conditions and the following disclaimer in the*     documentation and/or other materials provided with the distribution.*   * Neither the name of the organization nor the names of its contributors*     may be used to endorse or promote products derived from this software*     without specific prior written permission.**   See <http://www.opensource.org/licenses/bsd-license>*/#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"#include <iostream>
#include <fstream>
#include <sstream>
#include <direct.h>using namespace cv;
using namespace std;//const char *faceCascadeFilename = "C:\\opencv\\sources\\data\\lbpcascades\\lbpcascade_frontalface.xml";
const char *faceCascadeFilename = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt2.xml";
const char *eyeCascadeFilename1 = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml";               // Basic eye detector for open eyes only.
const char *eyeCascadeFilename2 = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_eye_tree_eyeglasses.xml";
const char *face_lib            = "face_train_img//";const int DESIRED_CAMERA_WIDTH = 640;
const int DESIRED_CAMERA_HEIGHT = 480;
const int Width = 92;
const int Height = 112;
int gender_width;
int gender_height;
int im_width;
int im_height;string g_listname_t[]=
{"Jack","William","huang","Barton"
};static Mat norm_0_255(InputArray _src) {Mat src = _src.getMat();// Create and return normalized image:Mat dst;switch(src.channels()) {case 1:cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);break;case 3:cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);break;default:src.copyTo(dst);break;}return dst;
}static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {std::ifstream file(filename.c_str(), ifstream::in);if (!file) {string error_message = "No valid input file was given, please check the given filename.";CV_Error(CV_StsBadArg, error_message);}string line, path, classlabel;while (getline(file, line)) {stringstream liness(line);getline(liness, path, separator);getline(liness, classlabel);if(!path.empty() && !classlabel.empty()) {images.push_back(imread(path, 0));labels.push_back(atoi(classlabel.c_str()));}}
}static void InitVideoCapture(VideoCapture &videoCapture, int CameraID)
{try{videoCapture.open(CameraID);}catch(cv::Exception& e){}if(!videoCapture.isOpened()){cerr << "ERROR: could not open Camera!" << endl;exit(1);}videoCapture.set(CV_CAP_PROP_FRAME_WIDTH, DESIRED_CAMERA_WIDTH);videoCapture.set(CV_CAP_PROP_FRAME_HEIGHT, DESIRED_CAMERA_HEIGHT);cout << "CameraID is :" << CameraID << endl;}static void InitDetectors(CascadeClassifier &faceCascade, CascadeClassifier &eyeCascade1, CascadeClassifier &eyeCascade2)
{try{faceCascade.load(faceCascadeFilename);}catch(cv::Exception& e){}if ( faceCascade.empty() ) {cerr << "ERROR: Could not load Face Detection cascade classifier [" << faceCascadeFilename << "]!" << endl;cerr << "Copy the file from your OpenCV data folder (eg: 'C:\\OpenCV\\data\\haarcascade_frontalface_alt2') into this WebcamFaceRec folder." << endl;exit(1);}cout << "Loaded the Face Detection cascade classifier [" << faceCascadeFilename << "]." << endl;// Load the Eye Detection cascade classifier xml file.try {   // Surround the OpenCV call by a try/catch block so we can give a useful error message!eyeCascade1.load(eyeCascadeFilename1);} catch (cv::Exception& e) {}if ( eyeCascade1.empty() ) {cerr << "ERROR: Could not load 1st Eye Detection cascade classifier [" << eyeCascadeFilename1 << "]!" << endl;cerr << "Copy the file from your OpenCV data folder (eg: 'C:\\OpenCV\\data\\haarcascades') into this WebcamFaceRec folder." << endl;exit(1);}cout << "Loaded the 1st Eye Detection cascade classifier [" << eyeCascadeFilename1 << "]." << endl;// Load the Eye Detection cascade classifier xml file.try {   // Surround the OpenCV call by a try/catch block so we can give a useful error message!eyeCascade2.load(eyeCascadeFilename2);} catch (cv::Exception& e) {}if ( eyeCascade2.empty() ) {cerr << "Could not load 2nd Eye Detection cascade classifier [" << eyeCascadeFilename2 << "]." << endl;// Dont exit if the 2nd eye detector did not load, because we have the 1st eye detector at least.//exit(1);}elsecout << "Loaded the 2nd Eye Detection cascade classifier [" << eyeCascadeFilename2 << "]." << endl;}void readDataTraining(Ptr<FaceRecognizer> &model,vector<Mat> &images,vector<int> &labels,string &filePath )
{// These vectors hold the images and corresponding labels.// Read in the data. This can fail if no valid// input filename is given.try {read_csv(filePath, images, labels);} catch (cv::Exception& e) {cerr << "Error opening file \"" << filePath << "\". Reason: " << e.msg << endl;// nothing more we can doexit(1);}// Quit if there are not enough images for this demo.if(images.size() <= 1) {string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";CV_Error(CV_StsError, error_message);}/*    Mat testSample = images[images.size() - 1];int testLabel = labels[labels.size() - 1];images.pop_back();labels.pop_back();*/model->train(images, labels);//int predictedLabel = model->predict(testSample);//// To get the confidence of a prediction call the model with:////      int predictedLabel = -1;//      double confidence = 0.0;//      model->predict(testSample, predictedLabel, confidence);///*string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);cout << result_message << endl;*/
}void preprocessing(Mat &srcimg, Mat &dstimg)
{Mat gray_img;if(srcimg.channels() ==3 ){cvtColor(srcimg,gray_img,CV_BGR2GRAY);}else if(srcimg.channels() ==4 ){cvtColor(srcimg,gray_img,CV_BGRA2GRAY);}else {gray_img = srcimg;}/*Mat equalized_Img;equalizeHist(gray_img,equalized_Img);*/dstimg = gray_img;
}void faceDectRecog(CascadeClassifier &faceCascade,Ptr<FaceRecognizer> &model,Ptr<FaceRecognizer> &gender_model,Ptr<FaceRecognizer> &fishermodel,Mat &orginalimg,Mat &dectImg)
{static int num = 0;int predictedLabel = 0;int gender_predict = 0;// Only search for just 1 object (the biggest in the image).int flags = CASCADE_FIND_BIGGEST_OBJECT;//smallest object SizeSize minFeatureSize = Size(20,20);// How detailed should the search be. Must be larger than 1.0.float searchScaleFactor = 1.1f;// How much the detections should be filtered out. This should depend on how bad false detections are to your system.// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.int minNeighbors = 8;vector<Rect> faces;faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,minNeighbors,flags,minFeatureSize);//faceCascade.detectMultiScale(equalized_Img, faces);/*  pca + LdaMat eigenvalues = gender_model->getMat("eigenvalues");//提取model中的特征值，该特征值默认由大到小排列  Mat W = gender_model->getMat("eigenvectors");//提取model中的特征向量，特征向量的排列方式与特征值排列顺序一一相应  int xth = 121;//打算保留前121个特征向量，代码中没有体现原因，但选择121是经过斟酌的，首先，在我的实验中，"前121个特征值之和/全部特征值总和>0.97"；其次，121=11^2，能够将结果表示成一个11*11的2维图像方阵，交给fisherface去计算。  //vector<Mat> reduceDemensionimages;//降维后的图像矩阵  Mat evs = Mat(W, Range::all(), Range(0, xth));//选择前xth个特征向量，其余舍弃Mat mean = gender_model->getMat("mean"); */int i = 0;for(i = 0; i < faces.size(); i++){Rect face_id = faces[i];Mat face = dectImg(face_id);Mat face_resized;Mat gender_resized;cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);cv::resize(face, gender_resized, Size(gender_width, gender_height), 1.0, 1.0, INTER_CUBIC);rectangle(orginalimg,face_id,Scalar(0,255,0),1);predictedLabel = model->predict(face_resized);string result_message;/*result_message = format("Predicted  = %d ", predictedLabel);cout << result_message << endl;*/
/*          PCA +LDAMat projection = subspaceProject(evs, mean, gender_resized.reshape(1,1));//做子空间投影  //reduceDemensionimages.push_back(projection.reshape(1,sqrt(xth*1.0)));Mat reduceDemensionimages = projection.reshape(1,sqrt(xth*1.0));gender_predict = fishermodel->predict(reduceDemensionimages);*/string box_text;box_text = format( "Prediction = " );if ( predictedLabel >= 0 && predictedLabel <=3 ){box_text.append( g_listname_t[predictedLabel] );}else box_text.append( "Unknown" );gender_predict = gender_model->predict(face_resized);if(gender_predict == 0){result_message = format("Predicted: female");box_text.append( "   female" );}else if (gender_predict == 1){result_message = format("Predicted: male");box_text.append( "   male" );}else result_message = format("Predicted: Unknow");cout << result_message << endl;// Calculate the position for annotated text (make sure we don't// put illegal values in there):int pos_x = std::max(face_id.tl().x - 10, 0);int pos_y = std::max(face_id.tl().y - 10, 0);// And now put it into the image:putText(orginalimg, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);}
}void faceDect(CascadeClassifier &faceCascade,Mat &orginalimg,Mat &dectImg)
{// Only search for just 1 object (the biggest in the image).int flags = CASCADE_FIND_BIGGEST_OBJECT;//smallest object SizeSize minFeatureSize = Size(20,20);// How detailed should the search be. Must be larger than 1.0.float searchScaleFactor = 1.1f;// How much the detections should be filtered out. This should depend on how bad false detections are to your system.// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.int minNeighbors = 6;vector<Rect> faces;faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,minNeighbors,flags,minFeatureSize);//faceCascade.detectMultiScale(equalized_Img, faces);int i = 0;for(i = 0; i < faces.size(); i++){Rect face_id = faces[i];rectangle(orginalimg,face_id,Scalar(0,255,0),1);}
}void CaptureFace(CascadeClassifier &faceCascade,Ptr<FaceRecognizer> &model,Mat &orginalimg,Mat &dectImg)
{static int num = 0;// Only search for just 1 object (the biggest in the image).int flags = CASCADE_FIND_BIGGEST_OBJECT;//smallest object SizeSize minFeatureSize = Size(20,20);// How detailed should the search be. Must be larger than 1.0.float searchScaleFactor = 1.1f;// How much the detections should be filtered out. This should depend on how bad false detections are to your system.// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.int minNeighbors = 6;vector<Rect> faces;faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,minNeighbors,flags,minFeatureSize);//faceCascade.detectMultiScale(equalized_Img, faces);int i = 0;for(i = 0; i < faces.size(); i++){Rect face_id = faces[i];Mat face = dectImg(face_id);Mat face_resized;cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);char c[4];itoa(num,c,10);string s = face_lib + (string)c + ".png";imwrite(s,face_resized);cout << "Capture the" << num << "face" << endl;cout << s << ";" << face_id << endl;num ++;rectangle(orginalimg,face_id,Scalar(0,255,0),1);}
}int main(int argc, const char *argv[])
{int mode;int i;// Get the path to your CSV.string fn_csv = string("at.txt");string gender_csv = string("gender.txt");string temp_csv = string("test.txt");CascadeClassifier faceCascade;CascadeClassifier eyeCascade1;CascadeClassifier eyeCascade2;VideoCapture videoCapture;Ptr<FaceRecognizer> model;Ptr<FaceRecognizer> temp_model;Ptr<FaceRecognizer> gender_model;int CameraID = 0;vector<Mat> images;vector<int> labels;vector<Mat> temp_images;vector<int> temp_labels;vector<Mat> gender_images;vector<int> gender_labels;cout << "Compiled with OpenCV version " << CV_VERSION << endl << endl;InitDetectors(faceCascade,eyeCascade1,eyeCascade2);InitVideoCapture(videoCapture,CameraID);printf("\n");printf("FaceDec and Recognition V0.1\n");printf("Usage: mode 0 : FaceDect; 1: train your own face; 2: Recognition \n");printf("please input mode\n");scanf("%d",&mode);//model = createEigenFaceRecognizer();temp_model = createEigenFaceRecognizer();model =createEigenFaceRecognizer();gender_model =createEigenFaceRecognizer();Ptr<FaceRecognizer> fishermodel = createFisherFaceRecognizer();  //gender_model = createEigenFaceRecognizer();if(mode == 3){readDataTraining(model,images,labels,fn_csv);readDataTraining(gender_model,gender_images,gender_labels,gender_csv);gender_width = gender_images[0].cols;gender_height = gender_images[0].rows;im_width = images[0].cols;im_height = images[0].rows;model->save("Face_recog.yml");gender_model->save("gender_recog.yml");}//readDataTraining(temp_model,temp_images,gender_labels,temp_csv);model->load("Face_recog.yml");gender_width = Width;gender_height = Height;im_width = Width;im_height = Height;gender_model->load("eigenface_gender.yml");//保存训练结果，供检測时使用  fishermodel->load("fisher.yml");printf("gender_width :%d gender_height :%d im_width: %d im_height:%d\n",gender_width,gender_height,im_width,im_height);int num = 0;Mat cameraFrame;if(mode == 4){read_csv(temp_csv, temp_images, temp_labels);for(i = 0; i < temp_images.size(); i ++){Mat temp_img;preprocessing(temp_images[i],temp_img);CaptureFace(faceCascade,temp_model,temp_images[i],temp_img);}}for(;;){videoCapture >> cameraFrame;if( cameraFrame.empty()){cerr << "Error : could not grap next frame " << endl;}Mat processFrame = cameraFrame.clone();Mat preprocess_img;preprocessing(processFrame,preprocess_img);switch(mode){case 0:faceDect(faceCascade,processFrame,preprocess_img);break;case 1:CaptureFace(faceCascade,model,processFrame,preprocess_img);case 2:faceDectRecog(faceCascade,model,gender_model,fishermodel,processFrame,preprocess_img);default:break;}imshow("face_recognizer",processFrame);char key = (char) waitKey(300);if(key == 27)break;}return 0;
}