Smart Attendance Monitoring System with Fingerprint and Face Recognition

This project implements a Smart Attendance Monitoring System that combines fingerprint and face recognition technologies to accurately mark and track attendance. The system offers a reliable, efficient, and user-friendly solution for attendance management, eliminating manual processes and proxy errors.

Paper

GRENZE International Journal of Engineering and Technology Page

Direct Download Paper

Abstract

The Smart Attendance Monitoring System employs biometric verification through fingerprint and face recognition modules to simplify attendance tracking. By combining these technologies, the system achieves an accuracy of 83%. The project addresses the challenges of manual attendance marking, proxy errors, and report generation. The system is designed for educational institutions, corporate offices, and various other applications.

Features

• Dual-layer biometric verification using fingerprint and face recognition.
• Efficient attendance marking and recording.
• Automated report generation.
• High accuracy in attendance tracking.

Prerequisites

• Raspberry Pi (tested on Raspberry Pi 4 model B with 8GB RAM).
• Fingerprint optical reader module (R307 used in testing).
• Python 3.8+ (compatible with Raspberry Pi OS and Windows 10/11).
• Required Python libraries (opencv, numpy, pandas, etc.).

Setup

1. Clone this repository to your Raspberry Pi or computer.
2. Install the necessary Python libraries using pip install -r requirements.txt.
3. Setup the hardware (Raspberry Pi, fingerprint module).
4. Customize settings and configurations in the code as required.

Usage

1. Run the main script: python attendance.py.
2. Use the GUI to enroll new users and start marking attendance.
3. The system supports both single-level and parallel attendance marking.
4. Attendance records are automatically generated and stored in CSV format.

Results

• Tested accuracy of face recognition: ~83%.
• Tested accuracy of fingerprint recognition: ~60% for correct fingerprints.
• Combined accuracy (weighted scheme): ~78.4%.

Conclusion and Future Scope

The Smart Attendance Monitoring System offers an innovative approach to attendance tracking using dual-layer biometric verification. The system's accuracy and user-friendly features make it suitable for various applications, including education, corporate, and access control. Future improvements include enhancing attendance counting efficiency and integration with existing systems.