描述
人脸识别包括四个步骤
- 人脸检测：定位人脸区域，只关心是不是脸；
- 人脸预处理：对人脸检测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如何识别；
- 人脸识别：识别当前人脸与数据库里的哪个人脸最相似。
人脸检测
OpenCV集成了基于PCA LDA 和LBP的人脸检测器，源文件自带很多各种训练好的检测器。下表是常用的XML文件
上面的XML文件可以检测正面人脸、眼睛或鼻子。检测人脸我采用的是第一个或第二个Harr人脸检测器。识别率比较好。
第一步：加载Harr人脸检测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：加载摄像头，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()函数执行直方图均衡化，提升对比度和亮度。
equalizeHist(gray_img,equalized_Img);第四步：检测人脸
上面已经创建了级联分类器并加载好XML文件，接着使用函数Classifier::detecMultiScale()函数来检测人脸。这个函数的参数说明：
a、minFeatureSize: 该参数决定最小的人脸大小，通常可以设为20*20或30*30像素。如果使用摄像机或移动设备检测，则人脸一般很接近摄像机，可把参数调大，80*80；
b、searchScaleFactor: 该参数决定有多少不同大小的人脸要搜索，通常设为1.1
c、minNeighbors： 该参数决定检测器如何确定人脸已经被检测到。通常设为3
d、flags: 该参数设定是否要查找所有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<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，需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参数进行匹配识别。人脸识别机器学习算法有很多，如SVM（支持向量机），ANN（人工神经网络）还有最常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() 函数即可进行训练模型。
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，一般是把模型保存下来，以免下次重复训练。直接加载模型即可。下一步就是人脸识别。同样，opencv把识别算法集成在FaceRecognizer类中，简单地调用FaceRecognizer::predict() 就可以识别。
int identity = model->predict(preprocessedFace);测试程序
/*
* 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);
}
else
cout << "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 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<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 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<Rect> 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对应
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/所有特征值总和>0.97"；其次，121=11^2，可以将结果表示成一个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 +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<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 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<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，供检测时使用
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;
}