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Hello everyone, I try to use this face recognition code below which I found on one of the Opencv documentation, but I have a problem when a new person face who is not in the recognition database is compared, the program confused saying this face is Mr.X or Mr.y or Mr.z who are already in the database. It works fine if face of the person to be compared is already in the face recognition database.

Please people, your help will kindly be appreciated.

#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"

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

using namespace cv;
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 != 2) {
        cout << "usage: " << argv[0] << " <csv.ext>" << endl;
        exit(1);
    }
    // Get the path to your CSV.
    string fn_csv = string(argv[1]);
    // These vectors hold the images and corresponding labels.
    vector<Mat> images;
    vector<int> labels;
    // Read in the data. This can fail if no valid
    // input filename is given.
    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);
    }
    // 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);
    }
    // Get the height from the first image. We'll need this
    // later in code to reshape the images to their original
    // size:
    int height = images[0].rows;
    // The following lines simply get the last images from
    // your dataset and remove it from the vector. This is
    // done, so that the training data (which we learn the
    // cv::FaceRecognizer on) and the test data we test
    // the model with, do not overlap.
    Mat testSample = images[images.size() - 1];
    int testLabel = labels[labels.size() - 1];
    images.pop_back();
    labels.pop_back();
    // The following lines create an LBPH model for
    // face recognition and train it with the images and
    // labels read from the given CSV file.
    //
    // The LBPHFaceRecognizer uses Extended Local Binary Patterns
    // (it's probably configurable with other operators at a later
    // point), and has the following default values
    //
    //      radius = 1
    //      neighbors = 8
    //      grid_x = 8
    //      grid_y = 8
    //
    // So if you want a LBPH FaceRecognizer using a radius of
    // 2 and 16 neighbors, call the factory method with:
    //
    //      cv::createLBPHFaceRecognizer(2, 16);
    //
    // And if you want a threshold (e.g. 123.0) call it with its default values:
    //
    //      cv::createLBPHFaceRecognizer(1,8,8,8,123.0)
    //
    Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
    model->train(images, labels);
    // The following line predicts the label of a given
    // test image:
    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;
    // Sometimes you'll need to get/set internal model data,
    // which isn't exposed by the public cv::FaceRecognizer.
    // Since each cv::FaceRecognizer is derived from a
    // cv::Algorithm, you can query the data.
    //
    // First we'll use it to set the threshold of the FaceRecognizer
    // to 0.0 without retraining the model. This can be useful if
    // you are evaluating the model:
    //
    model->set("threshold", 0.0);
    // Now the threshold of this model is set to 0.0. A prediction
    // now returns -1, as it's impossible to have a distance below
    // it
    predictedLabel = model->predict(testSample);
    cout << "Predicted class = " << predictedLabel << endl;
    // Show some informations about the model, as there's no cool
    // Model data to display as in Eigenfaces/Fisherfaces.
    // Due to efficiency reasons the LBP images are not stored
    // within the model:
    cout << "Model Information:" << endl;
    string model_info = format("\tLBPH(radius=%i, neighbors=%i, grid_x=%i, grid_y=%i, threshold=%.2f)",
            model->getInt("radius"),
            model->getInt("neighbors"),
            model->getInt("grid_x"),
            model->getInt("grid_y"),
            model->getDouble("threshold"));
    cout << model_info << endl;
    // We could get the histograms for example:
    vector<Mat> histograms = model->getMatVector("histograms");
    // But should I really visualize it? Probably the length is interesting:
    cout << "Size of the histograms: " << histograms[0].total() << endl;
    return 0;
}