⼈脸和性别识别（基于OpenCV）
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 ( pty() ) {
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;
}
rectangle函数opencv2、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<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 (pty()) {
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 </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\\l";
const char *faceCascadeFilename = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_l";
const char *eyeCascadeFilename1 = "C:\\opencv\\sources\\data\\haarcascades\\l";              // Basic eye detector for open eyes only.
const char *eyeCascadeFilename2 = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_eye_l";
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 = _Mat();
// 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:
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(!pty() && !pty()) {
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 ( pty() ) {
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_frontalfac
e_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 ( pty() ) {
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 ( pty() ) {
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    //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, shape(1,1));//做⼦空间投影
//reduceDemensionimages.push_shape(1,sqrt(xth*1.0)));
Mat reduceDemensionimages = shape(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("");
string temp_csv = string("");
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("l");
gender_model->save("l");
}
//readDataTraining(temp_model,temp_images,gender_labels,temp_csv);
model->load("l");
gender_width = Width;
gender_height = Height;
im_width = Width;
im_height = Height;
gender_model->load("l");//保存训练结果，供检測时使⽤
fishermodel->load("l");
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( pty()){
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;
}

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