描写叙述

人脸识别包含四个步骤

人脸检測：定位人脸区域，仅仅关心是不是脸；

人脸预处理：对人脸检測出来的图片进行调整优化。

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；

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 Size    Size 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
      
        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
      
        model;// Use OpenCV's new FaceRecognizer in the "contrib" module:model = Algorithm::create
       
        (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 
      
       . * 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 
       
         */#include "opencv2/core/core.hpp"#include "opencv2/contrib/contrib.hpp"#include "opencv2/highgui/highgui.hpp"#include 
        
         #include 
         
          #include 
          
           #include 
           
            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
            
             & images, vector
             
              & 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); } else cout << "Loaded the 2nd Eye Detection cascade classifier [" << eyeCascadeFilename2 << "]." << endl;}void readDataTraining(Ptr
              
                &model,vector
               
                 &images,vector
                
                  &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 do exit(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
                 
                   &model,Ptr
                  
                    &gender_model,Ptr
                   
                     &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 Size Size 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
                    
                      faces; faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor, minNeighbors,flags,minFeatureSize); //faceCascade.detectMultiScale(equalized_Img, faces);/* pca + Lda Mat 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
                     
                       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 +LDA Mat 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 Size Size 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
                      
                        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
                       
                         &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 Size Size 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
                        
                          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
                         
                           model; Ptr
                          
                            temp_model; Ptr
                           
                             gender_model; int CameraID = 0; vector
                            
                              images; vector
                             
                               labels; vector
                              
                                temp_images; vector
                               
                                 temp_labels; vector
                                
                                  gender_images; vector
                                 
                                   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
                                  
                                    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;}

你可能感兴趣的文章