基于OpenCV性别识别

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/zwhlxl/article/details/44401637

描述

所谓性别识别就是判断检测出来的脸是男性还是女性，是个二元分类问题。识别所用的算法可以是SVM，BP神经网络，LDA，PCA，PCA+LDA等等。OpenCV官网给出的文档是基于Fisherfaces检测器（LDA）方法实现的。链接：http://docs.opencv.org/modules/contrib/doc/facerec/tutorial/facerec_gender_classification.html#id5 。这篇博文（http://www.bytefish.de/blog/gender_classification/）中也是采用OpenCV官网的方法，据称有98%的正确率，我在百度图片找了一些数据测试了下，只有大概50%多的识别率。原因是他的数据集是经过严格标定的，好像是眼睛的是对齐的。实际应用中不太可能会遇到这种情况吧。CSDN还有两篇博客也介绍到这个性格识别（http://blog.csdn.net/kklots/article/details/8247738 http://blog.csdn.net/kklots/article/details/9285505）文章写得很好，一看就是大牛。博文中也是测试了LDA的方法，正确率也是出奇的低。采用的是PCA+LDA的方法。通过改进能达到接近90%的正确率。博文指出PCA+LDA比单纯的LDA和PCA识别率都高，但我对博文中的PCA+LDA程序和官网的PCA程序测试了下，发现PCA的正确率会高那么一两个点。难道又是数据的问题？

数据

采集数据一方面可以采用开源的人脸库，另一方面可以自己去百度图片下载图片。去百度或谷歌图片分别搜索“男明星头像”“女明星头像”的关键字批量下载，这里当然需要批量下载利器。然后利用人脸检测器过滤检测出头像，然后归一化检测出来的图像，保存在本地。这样基本的数据集就有了。当然我也会附上我采集的数据和工程文件（特此声明，所有图片均来自网络）

测试程序

创建CSV文件的python代码:

import sys
import os.path


if __name__ == "__main__":

    if len(sys.argv) != 3:
        print "usage: create_csv <base_path> <SAVE_FILE_NAME>"
        sys.exit(1)

    BASE_PATH=sys.argv[1]
    FILE_NAME = sys.argv[2]
    SEPARATOR=";"
    fh = open(FILE_NAME,'w')

    label = 0
    for dirname, dirnames, filenames in os.walk(BASE_PATH):
        for subdirname in dirnames:
            subject_path = os.path.join(dirname, subdirname)
            for filename in os.listdir(subject_path):
                abs_path = "%s/%s" % (subject_path, filename)
                ##print "%s%s%d" % (abs_path, SEPARATOR, label)
                ##print "%s  %s" % (dirname, subject_path)

                fh.write(abs_path)
                fh.write(SEPARATOR)
                if dirname.find("female") > 0 :
                    label = 1
                else:
                    label = 0
                fh.write(str(label))
                fh.write("\n")
    fh.close()

测试性别识别的程序

// gender.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include <opencv2/opencv.hpp>
#include <iostream>
#include <fstream>
#include <sstream>
#include <math.h>
int g_howManyPhotoForTraining   =   260;
//每个人取出8张作为训练
int g_photoNumberOfOnePerson    =   279;
//ORL数据库每个人10张图像
using namespace cv;
using namespace std;

static Mat norm_0_255(InputArray _src) {
    Mat src = _src.getMat();
    // 创建和返回一个归一化后的图像矩阵:
    Mat dst;
    switch(src.channels()) {
case1:
        cv::normalize(_src, dst, 0,255, NORM_MINMAX, CV_8UC1);
        break;
case3:
        cv::normalize(_src, dst, 0,255, NORM_MINMAX, CV_8UC3);
        break;
    default:
        src.copyTo(dst);
        break;
    }
    return dst;
}
//使用CSV文件去读图像和标签，主要使用stringstream和getline方法
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()));
        }
    }
}

void train_and_test_lda()
{
    string fn_csv = string("at.txt");
    //string fn_csv = string("feret.txt");
    vector<Mat> allImages,train_images,test_images;
    vector<int> allLabels,train_labels,test_labels;
    try {
        read_csv(fn_csv, allImages, allLabels);
    } catch (cv::Exception& e) {
        cerr <<"Error opening file "<< fn_csv <<". Reason: "<< e.msg << endl;
        // 文件有问题，我们啥也做不了了，退出了
        exit(1);
    }
    if(allImages.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);
    }

    for(int i=0 ; i<allImages.size() ; i++)
        equalizeHist(allImages[i],allImages[i]);

    int photoNumber = allImages.size();
    for(int i=0 ; i<photoNumber ; i++)
    {
        if((i%g_photoNumberOfOnePerson)<g_howManyPhotoForTraining)
        {
            train_images.push_back(allImages[i]);
            train_labels.push_back(allLabels[i]);
        }
        else
        {
            test_images.push_back(allImages[i]);
            test_labels.push_back(allLabels[i]);
        }
    }

    /*Ptr<FaceRecognizer> model = createEigenFaceRecognizer();//定义pca模型  
    model->train(train_images, train_labels);//训练pca模型，这里的model包含了所有特征值和特征向量，没有损失  
    model->save("eigenface.yml");//保存训练结果，供检测时使用  */

    Ptr<FaceRecognizer> fishermodel = createFisherFaceRecognizer();  
    fishermodel->train(train_images,train_labels);//用保存的降维后的图片来训练fishermodel，后面的内容与原始代码就没什么变化了  
    fishermodel->save("fisherlda.yml");
    int iCorrectPrediction = 0;
    int predictedLabel;
    int testPhotoNumber = test_images.size();
    for(int i=0;i<testPhotoNumber;i++)
    {
        predictedLabel = fishermodel->predict(test_images[i]);
        if(predictedLabel == test_labels[i])
            iCorrectPrediction++;
    }
    string result_message = format("Test Number = %d / Actual Number = %d.", testPhotoNumber, iCorrectPrediction);
    cout << result_message << endl;
    cout<<"accuracy = "<<float(iCorrectPrediction)/testPhotoNumber<<endl;
}

void train_and_test_pca()
{
    string fn_csv = string("at.txt");
    //string fn_csv = string("feret.txt");
    vector<Mat> allImages,train_images,test_images;
    vector<int> allLabels,train_labels,test_labels;
    try {
        read_csv(fn_csv, allImages, allLabels);
    } catch (cv::Exception& e) {
        cerr <<"Error opening file "<< fn_csv <<". Reason: "<< e.msg << endl;
        // 文件有问题，我们啥也做不了了，退出了
        exit(1);
    }
    if(allImages.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);
    }

    for(int i=0 ; i<allImages.size() ; i++)
        equalizeHist(allImages[i],allImages[i]);

    int photoNumber = allImages.size();
    for(int i=0 ; i<photoNumber ; i++)
    {
        if((i%g_photoNumberOfOnePerson)<g_howManyPhotoForTraining)
        {
            train_images.push_back(allImages[i]);
            train_labels.push_back(allLabels[i]);
        }
        else
        {
            test_images.push_back(allImages[i]);
            test_labels.push_back(allLabels[i]);
        }
    }

    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();//定义pca模型  
    model->train(train_images, train_labels);//训练pca模型，这里的model包含了所有特征值和特征向量，没有损失  
    model->save("eigenfacepca.yml");//保存训练结果，供检测时使用  
    int iCorrectPrediction = 0;
    int predictedLabel;
    int testPhotoNumber = test_images.size();
    for(int i=0;i<testPhotoNumber;i++)
    {
        predictedLabel = model->predict(test_images[i]);
        if(predictedLabel == test_labels[i])
            iCorrectPrediction++;
    }
    string result_message = format("Test Number = %d / Actual Number = %d.", testPhotoNumber, iCorrectPrediction);
    cout << result_message << endl;
    cout<<"accuracy = "<<float(iCorrectPrediction)/testPhotoNumber<<endl;
}

void train_and_test()
{
    string fn_csv = string("at.txt");
    //string fn_csv = string("feret.txt");
    vector<Mat> allImages,train_images,test_images;
    vector<int> allLabels,train_labels,test_labels;
    try {
        read_csv(fn_csv, allImages, allLabels);
    } catch (cv::Exception& e) {
        cerr <<"Error opening file "<< fn_csv <<". Reason: "<< e.msg << endl;
        // 文件有问题，我们啥也做不了了，退出了
        exit(1);
    }
    if(allImages.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);
    }

    for(int i=0 ; i<allImages.size() ; i++)
        equalizeHist(allImages[i],allImages[i]);

    int photoNumber = allImages.size();
    for(int i=0 ; i<photoNumber ; i++)
    {
        if((i%g_photoNumberOfOnePerson)<g_howManyPhotoForTraining)
        {
            train_images.push_back(allImages[i]);
            train_labels.push_back(allLabels[i]);
        }
        else
        {
            test_images.push_back(allImages[i]);
            test_labels.push_back(allLabels[i]);
        }
    }

    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();//定义pca模型  
    model->train(train_images, train_labels);//训练pca模型，这里的model包含了所有特征值和特征向量，没有损失  
    model->save("eigenface.yml");//保存训练结果，供检测时使用  
    Mat eigenvalues = 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 = 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  
    vector<Mat> testreduceDemensionimages;
    Mat evs = Mat(W, Range::all(), Range(0, xth));//选择前xth个特征向量，其余舍弃
    Mat mean = model->getMat("mean");  
    for(int i=0;i<train_images.size();i++)  
    { 
        Mat projection = subspaceProject(evs, mean, train_images[i].reshape(1,1));//做子空间投影  
        reduceDemensionimages.push_back(projection.reshape(1,sqrt(xth*1.0)));//将获得的子空间系数表示映射成2维图像，并保存起来  
    }  

    for(int i=0;i<test_images.size();i++)  
    { 
        Mat projection = subspaceProject(evs, mean, test_images[i].reshape(1,1));//做子空间投影  
        testreduceDemensionimages.push_back(projection.reshape(1,sqrt(xth*1.0)));//将获得的子空间系数表示映射成2维图像，并保存起来  
    }

    Ptr<FaceRecognizer> fishermodel = createFisherFaceRecognizer();  
    fishermodel->train(reduceDemensionimages,train_labels);//用保存的降维后的图片来训练fishermodel，后面的内容与原始代码就没什么变化了  
    fishermodel->save("fisher.yml");
    int iCorrectPrediction = 0;
    int predictedLabel;
    int testPhotoNumber = test_images.size();
    for(int i=0;i<testPhotoNumber;i++)
    {
        predictedLabel = fishermodel->predict(testreduceDemensionimages[i]);
        if(predictedLabel == test_labels[i])
            iCorrectPrediction++;
    }
    string result_message = format("Test Number = %d / Actual Number = %d.", testPhotoNumber, iCorrectPrediction);
    cout << result_message << endl;
    cout<<"accuracy = "<<float(iCorrectPrediction)/testPhotoNumber<<endl;
}


void test_pca()
{
    string fn_csv = string("test.txt");
    vector<Mat> allImages;
    vector<int> allLabels;
    try {
        read_csv(fn_csv, allImages, allLabels);
    } catch (cv::Exception& e) {
        cerr <<"Error opening file "<< fn_csv <<". Reason: "<< e.msg << endl;
        // 文件有问题，我们啥也做不了了，退出了
        exit(1);
    }
    if(allImages.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);
    }

    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();//定义pca模型  
    model->load("eigenfacepca.yml");//保存训练结果，供检测时使用  

    int iCorrectPrediction = 0;
    int predictedLabel;
    int testPhotoNumber = allImages.size();
    for(int i=0;i<testPhotoNumber;i++)
    {
        predictedLabel = model->predict(allImages[i]);
        if(predictedLabel == allLabels[i])
            iCorrectPrediction++;
    }

    string result_message = format("Test Number = %d / Actual Number = %d.", testPhotoNumber, iCorrectPrediction);
    cout << result_message << endl;
    cout<<"accuracy = "<<float(iCorrectPrediction)/testPhotoNumber<<endl;   
}

void test()
{
    string fn_csv = string("test.txt");
    vector<Mat> allImages;
    vector<int> allLabels;
    try {
        read_csv(fn_csv, allImages, allLabels);
    } catch (cv::Exception& e) {
        cerr <<"Error opening file "<< fn_csv <<". Reason: "<< e.msg << endl;
        // 文件有问题，我们啥也做不了了，退出了
        exit(1);
    }
    if(allImages.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);
    }

    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();//定义pca模型  
    model->load("eigenface.yml");//保存训练结果，供检测时使用  
    Mat eigenvalues = 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 = 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 = model->getMat("mean");  
    for(int i=0;i<allImages.size();i++)  
    { 
        Mat projection = subspaceProject(evs, mean, allImages[i].reshape(1,1));//做子空间投影  
        reduceDemensionimages.push_back(projection.reshape(1,sqrt(xth*1.0)));//将获得的子空间系数表示映射成2维图像，并保存起来  
    }  



    Ptr<FaceRecognizer> fishermodel = createFisherFaceRecognizer();  

    fishermodel->load("fisher.yml");
    int iCorrectPrediction = 0;
    int predictedLabel;
    int testPhotoNumber = allImages.size();
    for(int i=0;i<testPhotoNumber;i++)
    {
        predictedLabel = fishermodel->predict(reduceDemensionimages[i]);
        if(predictedLabel == allLabels[i])
            iCorrectPrediction++;
    }

    string result_message = format("Test Number = %d / Actual Number = %d.", testPhotoNumber, iCorrectPrediction);
    cout << result_message << endl;
    cout<<"accuracy = "<<float(iCorrectPrediction)/testPhotoNumber<<endl;   
}

int main() {

    cout<<"lda = "<<endl;
    train_and_test_lda();
    cout<<"pca = "<<endl;
    train_and_test_pca();
    cout<<"pca+lda = "<<endl;
    train_and_test();
    /*test();
    test_pca();*/
    return 0 ;
}

整个工程文件和数据下载链接 http://download.csdn.net/detail/zwhlxl/8510649