c++ - 如何提高 EigenFaceRecognizer 的准确率，将两个人识别为一个人？

Question

我正在尝试使用 C++ 中的 EigenFaceRecognizer 识别正面。

问题是:

1) 在高阈值时，两个人被识别为同一个人，并且还识别出"new"面孔，而不是将其声明为新面孔

2)在低阈值下，将训练集中已有的人脸识别为新人脸

3) 也会遇到误报。虽然不是问题，但如果建议一种简单的方法来减少它们，我们将不胜感激>

有什么方法可以改进识别器以准确识别人脸吗？

以下是我正在做的。

 #include<opencv2\opencv.hpp>                //For opencv functions
#include<opencv2\highgui\highgui.hpp>       //For window based functions
#include<fstream>                           //For dealing with I/O operations on file

using namespace std;
using namespace cv;


// Function to read the File containing paths and labels of the training images and push them into images and     labels vector
static void read_data(vector <Mat> & images,vector <int>& labels, char separator=' ')
{
ifstream file("images.txt");   //images.txt contains paths and labels separated by a space
string line;
string a[2];

 while(getline(file,line))  // read images.txt line by line 
 {
     int i=0;
stringstream iss(line);
while (iss.good() && i < 2)
{
    iss>>a[i]; 
    ++i;
}
images.push_back(imread(a[0],CV_LOAD_IMAGE_GRAYSCALE)); // a[0] = "path of images"
labels.push_back(atoi(a[1].c_str()));  //a[1] = "labels"
}

file.close();
  }


  // Function to take input from webcam and recognize faces 
   int face_recognition::face_rec(int time_flag, int trigger_flag)
  {

 vector<Mat> images;    //stores the paths of all images
vector<int> labels;    //stores the corresponding labels

//function call to function read_data
read_data(images,labels);    

//take the size of the sample images
int im_width = images[0].cols;           
int im_height = images[0].rows;

//threshold is the minimum value of magnitude of vector of EigenFaces
double threshold=10.0;    

//create instance of EigenFaceRecognizer
Ptr<FaceRecognizer> model = createEigenFaceRecognizer(10,threshold);  
double current_threshold =model->getDouble("threshold");

// set a threshold value, for face prediction
model->set("threshold",5000.0);      

// train the face recognizer using the sample images
model->train(images,labels);         

// Create face_cascade to detect people
CascadeClassifier face_cascade;
if(!face_cascade.load("haarcascade_frontalface_default.xml"))   // load     haarcascade_frontaface_default.xml
{ 
cout<<"ERROR Loading cascade file";
return 1;
}

// capture the video input from webcam
VideoCapture capture(CV_CAP_ANY);    

capture.set(CV_CAP_PROP_FRAME_WIDTH, 320);
capture.set(CV_CAP_PROP_FRAME_HEIGHT, 240); 

Size frameSize(static_cast<int>(320), static_cast<int>(240));

//initialize the VideoWriter object
VideoWriter oVideoWriter ("MyVideo.avi", CV_FOURCC('P','I','M','1'), 20, frameSize, true);  // video is save in the  VS  project

if(!capture.isOpened())
{
    cout<<"Error in camera";
return 1;
}

Mat cap_img, gray_img;

//store the detected faces
vector<Rect> faces;   
while(1)
{
//capture frame by frame in cap_img
capture>>cap_img;   
waitKey(10);


// Image conversion: Color to Gray
cvtColor(cap_img,gray_img,CV_BGR2GRAY);   

//Histogram Equilization to increase contrast by stretching intensity ranges
equalizeHist(gray_img,gray_img);      




// detects faces in the frame
//CV_HAAR_SCALE_IMAGE to scale the size of the detect face 
//CV_HAAR_DO_CANNY_PRUNING to increase speed as it skips image regions that are unlikely to contain a face
face_cascade.detectMultiScale(gray_img,faces,1.1,10,CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING, Size(20,20),Size(300,300));  

Mat Normalized;

//Loop over the detected faces
for(int i=0;i<faces.size();i++)
{
    Rect face_i = faces[i];
    Mat face = gray_img(face_i);

    Mat face_resized;

    //resize the detected face to the size of sample images
    resize(face,face_resized, Size(im_width,im_height),1.0,1.0,INTER_CUBIC);  


    // predict the person the face belongs to, returns label
    int predicted_label = -1;
    predicted_label=model->predict(face_resized); 

    // Draws a rectangle around the faces
    rectangle(cap_img,face_i, CV_RGB(0,255,0),1);   

    //text to be put with the face, by default "new" for new faces
    string box_text=format("new");  

    // Change the text based on label
    if(predicted_label>-1)
        switch(predicted_label)
        {
            case 0:box_text = format("keanu");
                   break;
            case 1:box_text = format("selena");
                   break;
            case 2:box_text = format("shubham");
                   break;
        }


    // calculate the coordinates to put the text based on the postion of the face 
    int pos_x = max(face_i.tl().x - 10, 0);
    int pos_y = max(face_i.tl().y - 10, 0);

    // put text on the output screen
    putText(cap_img, box_text , Point(pos_x,pos_y), FONT_HERSHEY_PLAIN,0.8, CV_RGB(0,255,0), 1,CV_AA);  

    if (box_text=="new")
    {
       oVideoWriter.write(cap_img); //writer the frame into the file
    }
}

// show the frame on the result window
imshow("Press Esc to exit",cap_img); 

if(waitKey(30)==27)         
    break; 
}

返回0；

Answer 1

我遇到了同样的问题。答案 ( and code ) 在 Mastering OpenCV 的第 8 章中很棒的 Shervin Emami 的书。 Here's his blog post关于这个问题。

主要是对人脸做一些预处理，包括以下几个步骤

1. 训练时，在连续帧之间进行差异处理，并且仅在帧与前一帧明显不同时才对其进行预处理
2. 将图像和镜像版本都添加到训练集中，以便您拥有更多训练数据，还可以处理向左或向右看的人脸
3. 直方图均衡化以提高面部的对比度和亮度
4. 面部经过缩放、旋转、平移，以便眼睛对齐。
5. 从面部图像中去除额头、下巴、耳朵和背景
6. 分别对左右脸进行直方图均衡化
7. 使用双边滤波器平滑或去除噪声
8. 用于去除剩余头发和背景的椭圆蒙版

干杯。