Face Recognition Attendance System

Overview

The Face Recognition Attendance System is a Python-based application designed to automate attendance tracking using facial recognition technology. The system leverages OpenCV and face recognition libraries to identify and log the presence of individuals in real-time, providing an efficient and reliable method for managing attendance.

Features

• Real-Time Face Detection: Utilizes pre-trained deep learning models to detect and recognize faces in real-time.
• Attendance Logging: Automatically records attendance by matching recognized faces against a pre-defined database.
• User Management: Add, update, and delete user profiles with associated facial images.
• Attendance Reports: Generate and export attendance reports in various formats (CSV, Excel).
• User-Friendly Interface: Simple and intuitive GUI for ease of use and navigation.

Installation

To set up and run the Face Recognition Attendance System, follow these steps:

1. Clone the Repository:

   git clone https://github.com/CharanKocharla13/Face-Recognition-Attendence-System.git
   cd Face-Recognition-Attendence-System

2. Create a Virtual Environment (Optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies:

   Ensure you have pip installed, then run:

   pip install -r requirements.txt

4. Download Pre-Trained Models:

   Follow the instructions in models/README.md to download and place pre-trained models needed for face detection and recognition.

5. Configure the System:

   Edit config/config.json to set up your database and user management options.

6. Run the Application:

   python main.py

Usage

• Adding New Users: Use the add_user function to capture and save new user faces.
• Starting Attendance: Run the main application to start face detection and attendance tracking.
• Viewing Reports: Access the generated reports through the reports directory.

Contributing

Contributions are welcome! Please follow these guidelines:

• Fork the repository and create a feature branch.
• Write tests for your changes.
• Ensure all tests pass and run lint to check code quality.
• Submit a pull request with a detailed description of your changes.

License

This project is licensed under the MIT License - see the LICENCE file for details.