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Real-Time Face Recognition System

This project implements a real-time face recognition system using Python and OpenCV. It includes scripts for face detection, model training, and real-time recognition.

Project Overview

The system uses a Haar cascade classifier for face detection and a custom-trained model for face recognition. The following files are included in the project:

  • Face recognition.py: The main script for running real-time face recognition. This script captures video from the webcam, detects faces, and identifies them using the trained model.
  • haarcascade_frontalface_default.xml: An XML file containing the Haar cascade classifier for detecting frontal faces. This file is used by OpenCV for face detection.
  • Model Trainer.py: A script for training the face recognition model. This script takes sample images of faces and trains a model that can be used for recognition.
  • Sample generator.py: A script for generating sample images of faces. This script captures images from the webcam and saves them to the samples/ directory.
  • samples/: A directory containing sample images of faces used for training the model.
  • trainer/: A directory containing the trained face recognition model.

How to Use

  1. Generate Sample Images: Run Sample generator.py to capture sample images of faces.
  2. Train the Model: Run Model Trainer.py to train the face recognition model using the sample images.
  3. Run Face Recognition: Run Face recognition.py to start real-time face recognition.

Dependencies

  • Python 3.x
  • OpenCV
  • numpy

Notes

  • Ensure that you have the required dependencies installed before running the scripts.
  • The accuracy of the face recognition system depends on the quality and quantity of the sample images used for training.