如何从视频中识别人脸识别中的未知人？

Posted 2023-03-16

【中文标题】如何从视频中识别人脸识别中的未知人？

【英文标题】：How to identify unknown persons in facerecognition from videos?

【发布时间】：2017-08-28 11:28:29

【问题描述】：

我正在使用 Philipp Wagner 的视频中的面部识别，我更新了代码以使用 opencv 3.2，之后我很难创建合适的面部数据库，但我的问题是我该如何给出未知人的值？到目前为止，当我运行我的代码时，它会从我的数据库中为未知人提供一个值，我为自己使用“0”，为另一个人使用“1”。例如，对于未知主题，我如何将其设置为“-1”？到目前为止，这是我的代码，我尝试使用阈值但没有得到任何结果。

#include "opencv2/core.hpp"
#include "opencv2/face.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace cv::face;
using namespace std;

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()));
        
    


int main(int argc, const char *argv[]) 
    // Check for valid command line arguments, print usage
    // if no arguments were given.
    if (argc != 4) 
        cout << "usage: " << argv[0] << " </path/to/haar_cascade> </path/to/csv.ext> </path/to/device id>" << endl;
        cout << "\t </path/to/haar_cascade> -- Path to the Haar Cascade for face detection." << endl;
        cout << "\t </path/to/csv.ext> -- Path to the CSV file with the face database." << endl;
        cout << "\t <device id> -- The webcam device id to grab frames from." << endl;
        exit(1);
    
    // Get the path to your CSV:
    string fn_haar = string(argv[1]);
    string fn_csv = string(argv[2]);
    int deviceId = atoi(argv[3]);
    // These vectors hold the images and corresponding labels:
    vector<Mat> images;
    vector<int> labels;
    // Read in the data (fails if no valid input filename is given, but you'll get an error message):
    try 
        read_csv(fn_csv, images, labels);
     catch (cv::Exception& e) 
        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
        // nothing more we can do
        exit(1);
    
    // Get the height from the first image. We'll need this
    // later in code to reshape the images to their original
    // size AND we need to reshape incoming faces to this size:
    int im_width = images[0].cols;
    int im_height = images[0].rows;
    // Create a FaceRecognizer and train it on the given images:
    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
    model->train(images, labels);
    // That's it for learning the Face Recognition model. You now
    // need to create the classifier for the task of Face Detection.
    // We are going to use the haar cascade you have specified in the
    // command line arguments:
    //
    CascadeClassifier haar_cascade;
    haar_cascade.load(fn_haar);
    // Get a handle to the Video device:
    VideoCapture cap(deviceId);
    // Check if we can use this device at all:
    if(!cap.isOpened()) 
        cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
        return -1;
    
    // Holds the current frame from the Video device:
    Mat frame;
    for(;;) 
        cap >> frame;
        // Clone the current frame:
        Mat original = frame.clone();
        // Convert the current frame to grayscale:
        Mat gray;
        cvtColor(original, gray, CV_BGR2GRAY);
        // Find the faces in the frame:
        vector< Rect_<int> > faces;
        haar_cascade.detectMultiScale(gray, faces);
        // At this point you have the position of the faces in
        // faces. Now we'll get the faces, make a prediction and
        // annotate it in the video. Cool or what?
        for(int i = 0; i < faces.size(); i++) 
            // Process face by face:
            Rect face_i = faces[i];
            // Crop the face from the image. So simple with OpenCV C++:
            Mat face = gray(face_i);
            // Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily
            // verify this, by reading through the face recognition tutorial coming with OpenCV.
            // Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the
            // input data really depends on the algorithm used.
            //
            // I strongly encourage you to play around with the algorithms. See which work best
            // in your scenario, LBPH should always be a contender for robust face recognition.
            //
            // Since I am showing the Fisherfaces algorithm here, I also show how to resize the
            // face you have just found:
            Mat face_resized;
            cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
            // Now perform the prediction, see how easy that is:
            int prediction = model->predict(face_resized);
            // And finally write all we've found out to the original image!
            // First of all draw a green rectangle around the detected face:
            rectangle(original, face_i, CV_RGB(0, 255,0), 1);
            // Create the text we will annotate the box with:
            string box_text = format("Prediction = %d", prediction);
            // Calculate the position for annotated text (make sure we don't
            // put illegal values in there):
            int pos_x = std::max(face_i.tl().x - 10, 0);
            int pos_y = std::max(face_i.tl().y - 10, 0);
            // And now put it into the image:
            putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
        
        // Show the result:
        imshow("face_recognizer", original);
        // And display it:
        char key = (char) waitKey(20);
        // Exit this loop on escape:
        if(key == 27)
            break;
    
    return 0;

【参考方案1】：

阅读此文档：Fisher Face Recognizer。阅读您使用的每种方法。这应该会为您提供解决问题所需的信息。

来自model->set上的文档：如果到最近邻的距离大于阈值，则该方法返回-1。在您的情况下，您没有收到任何 -1 的返回，这意味着您的阈值可能设置为高，这将允许不相似的面孔返回正匹配。

您似乎尚未设置阈值变量。尝试使用：model->set("threshold", DOUBLE_VALUE_HERE); 将阈值设置为较低的值。

0.0 的阈值几乎总是会返回 -1，因为图像在距离 > 0.0 时总是会有细微差别。尝试不同的阈值，看看是否能得到你想要的结果。我建议从 5.0: model->set("threshold", 5.0); 的值开始，然后从那里向上或向下工作。

【讨论】：

谢谢你，你是一个救生员，我用你的方法测试，但是当我给阈值一个小值时（这意味着如果我得到一张未知的脸，我想得到返回的值）我得到值“0”而不是“-1”假设我的面部数据库中没有“0”标签，这是否意味着它有效？

我的建议是让程序在没有命令行参数的情况下运行，然后从那里扩展。例如：首先手动为标签 0 加载同一人的多张图像，为标签 1 加载不同人的多张图像，然后与第三张静止图像进行比较。一旦你能得到它的工作，它会更容易扩展

我整理了这个示例程序，它可以在没有网络摄像头的情况下完成您想要做的事情。 My github link。它要简单得多，但它显示了阈值的重要性。

当我实际将一个人的标签设置为 0 并将另一个标签设置为 1 时，将阈值设置为 0.0 意味着它不会识别任何人，因此它会给出它不识别的 -1 值，我'm using : Ptr model = createLBPHFaceRecognizer(1,8,8,8,00.00) 其中 00.00 是阈值