public partial class Biometric : Form
{
    MCvFont font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_TRIPLEX, 0.6d, 0.6d);
    HaarCascade faceDetected;
    Image<Bgr, Byte> frame;
    Capture camera;
    Image<Gray, byte> result;
    Image<Gray, byte> trainedFace = null;
    Image<Gray, byte> grayFace = null;
    List<Image<Gray, byte>> trainingImage = new List<Image<Gray, byte>>();
    List<string> labels = new List<string>();
    List<string> users = new List<string>();
    int count, numLabeles, t;
    string name, names = null;
    
    public Biometric()
    {
        InitializeComponent();
        student = LoadDetails(Register.StudentId);
        StartWebcam();
    }
    
    private void StartWebcam()
    {
        try
        {
            faceDetected = new HaarCascade("haarcascade_frontalface_default.xml");
            camera = new Capture();
            camera.QueryFrame();
            Application.Idle += new EventHandler(FrameProcedure);
        }
        catch (Exception ex)
        {
            MessageBox.Show(ex.Message);
        }
    }
    
     private void FrameProcedure(object sender, EventArgs e)
    {
        users.Add("");
        frame = camera.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
        grayFace = frame.Convert<Gray, Byte>();
        MCvAvgComp[][] faceDetectedNow = grayFace.DetectHaarCascade(faceDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));

        if (faceDetectedNow[0].Count() > 1)
        {
            AbortRegistraction(student.StudentId);
            LogEvent(student.StudentId, "Two person detected. registration aborted");
            MessageBox.Show("Two person detected. registration aborted");
            Login login = new Login();
            login.Show();
            this.Close();
        }

        foreach (MCvAvgComp f in faceDetectedNow[0])
        {
            result = frame.Copy(f.rect).Convert<Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
            frame.Draw(f.rect, new Bgr(Color.Green), 3);
            if (trainingImage.ToArray().Length != 0)
            {
                MCvTermCriteria termCriterias = new MCvTermCriteria(count, 0.001);
                EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImage.ToArray(), labels.ToArray(), 1500, ref termCriterias);
                name = recognizer.Recognize(result);
                frame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Red));
            }
            users.Add("");
        }
        cameraBox.Image = frame;
        name = "";
        users.Clear();
    }
}