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.