Automated Attendance System Using Facial Recognition

Overview

The Automated Attendance System project aims to transition from a manual attendance system to an automated facial biometric attendance system. This project was initiated in response to a proposal from my college to enhance attendance tracking by integrating facial recognition with existing infrastructure.

Objective

• Train a facial recognition model and integrate it with the college’s camera infrastructure.
• Ensure accurate and efficient attendance tracking through biometric facial recognition.

Approach

• Face Detection: Used YOLOv8 for detecting faces in real-time.
• Face Recognition: Implemented FaceNet for recognizing and matching faces from the captured images.
• Training Accuracy: Achieved a 92% accuracy on the validation set after training with a dataset of students' facial images, captured under different orientations.

Challenges Encountered

• Camera Infrastructure Integration: Unable to integrate the system with the college's existing camera infrastructure due to challenges such as:
  • Lighting Variations: Different lighting conditions affecting recognition accuracy.
  • Distance and Angle: Difficulty in recognizing facial patterns due to the camera being mounted at a fixed distance and angle.

Solution and Current Implementation

• Alternative Solution: Since integration with the college's camera system wasn't feasible at the time, I proposed a different approach:
  • Handheld Biometric Devices: The model was successfully integrated into handheld biometric devices, which are now in use within the campus for automated attendance tracking.

Technologies Used

• YOLOv8 (for face detection)
• FaceNet (for face recognition)
• Python (for model training and integration)

How to Run the Project

1. Clone the repository:

   git clone https://github.com/your-username/automated-attendance-system.git

2. Install dependencies:

   pip install -r requirements.txt

3. Train the face recognition model:
   • Ensure you have the student facial image dataset available.
   • Run the training script for YOLOv8 and FaceNet integration.
4. For system integration:
   • Follow the instructions in the integration.md for deploying the model into handheld biometric devices.

Contributions

Feel free to fork this project, submit issues, or propose improvements. This project was developed as a learning experience and remains an ongoing exploration into effective biometric systems.

License

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

Contact

For further information, feel free to reach out via email or GitHub issues.