A dual-authentication attendance system

Face Recognition and Fingerprint-Based Attendance System

Challenge

Attendance tracking in academic and corporate environments still relies heavily on manual registers, swipe cards, or single-factor biometric systems. These approaches suffer from issues like proxy attendance, badge sharing, human error, and long queues during peak hours. Institutions needed a more reliable and tamper-resistant method that ensured accurate identity verification without compromising convenience.

Our Solution

We developed a prototype attendance system that integrates face recognition and fingerprint biometrics, providing dual-layer authentication for significantly higher reliability.

Key aspects of the solution included:

• Dual-Biometric Verification

  The system first detects and recognizes a person’s face using trained deep learning models, and then validates identity through a fingerprint scan—eliminating the possibility of proxy attendance.

• Automated Attendance Logging

  Once verified, the system records attendance instantaneously in a structured dataset, making it easy to retrieve reports for administrative use.

• Robust Recognition Pipeline

  The facial recognition module included steps like image preprocessing, alignment, feature extraction, and real-time matching, ensuring high accuracy even in varied lighting conditions.

• Efficient Prototype Development

  We managed both the research funding and the complete development lifecycle, delivering a working prototype suitable for institutional deployment and academic evaluation.

This combined biometric approach significantly enhances security and reliability compared to single-factor systems.

Impact

The system reduces fraudulent attendance, speeds up verification, and provides an accurate digital trail for institutions. By combining two independent biometric signals, the solution is resilient against spoofing and is scalable for classrooms, labs, and workplace environments with high throughput requirements.

Outcome

The project was presented at ACE2023 – Thirteenth International Conference on Advances in Computer Engineering and published in the Grenze International Journal of Engineering and Technology (GIJET). This highlights the system’s effectiveness and its potential for real-world adoption in secure attendance environments.