人脸和性别识别（基于OpenCV）
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 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，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 .
* 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 &model,vector &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 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 &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 model;
Ptr temp_model;
Ptr gender_model;
int CameraID = 0;
vector 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;
}