Face Recognition Attendance System

Overview

This project is a Facial Biometric Attendance System designed for real-time and efficient attendance tracking. The system integrates advanced face recognition algorithms and edge computing, using RetinaFace, MTCNN, OpenCV, PyTorch, and Jetson Nano for high-performance face detection and recognition. It effectively manages a 500-person facial database, deployed across campus and endorsed by college authorities.

Features

• Automated Attendance Tracking: Uses facial recognition to streamline attendance management.
• Advanced Face Detection: Implements RetinaFace and MTCNN for high-accuracy detection.
• Edge Computing: Utilizes Jetson Nano for real-time, on-device processing.
• Database Integration: Efficiently manages a 500-person facial database.
• Network Camera Support: Works with campus-wide surveillance cameras.

Technologies Used

• Languages: C++, Python, PHP
• Face Recognition: RetinaFace, MTCNN, OpenCV, PyTorch
• Hardware: Jetson Nano
• Database & Storage: PHP & Custom Storage Solutions

Installation & Setup

1. Clone the repository:

   git clone https://github.com/your-username/face-recognition-attendance.git
   cd face-recognition-attendance

2. Install required dependencies:

   pip install -r requirements.txt  # For Python dependencies

3. Ensure Jetson Nano is set up with the required libraries.
4. Run the application:

   python main.py

Contributing

Feel free to submit issues or pull requests to enhance the project.

License

This project is open-source and available under the MIT License.