Easyfid

Face Recognition System

Overview

This project implements a face recognition system in Python using OpenCV. It allows users to capture facial images, train a recognizer on these images, and then use the trained model to recognize faces in real-time.

Features

• Capture multiple facial images per user.
• Train a Local Binary Patterns Histograms (LBPH) face recognizer.
• Real-time face recognition with confidence scoring.

Requirements

• Python 3.x
• OpenCV (cv2) library
• NumPy
• Pillow (PIL)

Installation

• Clone the repository to your local machine: git clone https://github.com/nachovoss/easyfid.git

• Install the required packages:

   pip install numpy opencv-python pillow

or
```
pip install -r requirements.py
```

Usage

To use the face recognition system, follow these steps:

1. Run the main script:

   python Example.py

2. Follow the prompts to capture images, train the recognizer, and start the face recognition process:

   • Enter the number of pictures per person.
   • Enter the number of users for creating the dataset.
   • Train the recognizer.
   • Enter the number of users for recognition and their names.

Contributing

Contributions to this project are welcome. Please feel free to fork the repository, make changes, and submit a pull request.

License

MIT License