Biometric Attendance System

This paper presents the design, implementation, and evaluation of a fingerprint-based Biometric Attendance System tailored for educational institutions. The system integrates an optical fingerprint sensor with an Arduino microcontroller and HC-05 Bluetooth module to capture and transmit biometric templates to a Node.js WebSocket server and Firebase Firestore backend. A React.js web interface provides real-time attendance tracking, reporting, and administrative control. Performance testing with twenty students achieved an end-to-end attendance cycle in 8–10 minutes, demonstrating marked improvements over traditional manual methods. The system's security architecture is fortified through end-to-end encryption and token-based authentication, addressing critical vulnerabilities in student data protection. Performance metrics indicate a False Rejection Rate (FRR) of <2% and a False Acceptance Rate (FAR) of <0.5%, positioning the system as a reliable alternative to manual and card-based monitoring

Biometric Attendance, Fingerprint Recognition

1. Oriakor, C. T., Ayogu, C. K., Olelewe, C. J., Anoliefo, E., Ibam, E. O., Ogba, K. T. U., Atama, C., Ugwu, D. C., Igwe, N. J., Omeh, C. B., Kanu, C. C., Abu, H. S., & Onyishi, I. E. (2025). Which method of attendance-taking is superior? A systematic review of class attendance monitoring systems. Ikenga International Journal of Institute of African Studies, 26(1). [Google Scholar] [Crossref]

2. Salunkhe, A., Pawar, V., Pise, P., & Zambre, S. (2025). A review on real-time RFID-based smart attendance systems for efficient record management. Medical Science Journal for Advance Research, 2(2), 32–46 [Google Scholar] [Crossref]

3. Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4–20. [Google Scholar] [Crossref]

4. Li, Q., & Luo, J. (2020). A survey of facial and iris recognition for automated attendance systems. Pattern Recognition Letters, 135, 201–210. [Google Scholar] [Crossref]

5. Panchbhai, V., Waghmare, O., & Patel, V. (2024, November 21). Smart attendance system: An Internet of Things (IoT)-enabled concept. Cureus Journal of Computer Science. https://doi.org/10.7759/s44389-024-00164-z. [Google Scholar] [Crossref]

6. Arpitha, K. M., Chandrika, R., & Ashwini, V. K. (2025, May). Web-based student attendance management system: An automated approach for efficient academic monitoring. International Research Journal of Modernization in Engineering Technology and Science, 7(5). https://doi.org/10.56726/IRJMETS76408 [Google Scholar] [Crossref]

7. Habila, M., Francisca, F. N., Ishaya, L., Charles, H. P., et al. (2025). Smart real-time attendance system for Nigerian universities. Journal of Information and Organizational Sciences, 49(1), 121–138. https://doi.org/10.31341/jios.49.1.8. [Google Scholar] [Crossref]

8. Habila, M., Francisca, F. N., Ishaya, L., Charles, H. P., et al. (2025). Smart real-time attendance system for Nigerian universities. Journal of Information and Organizational Sciences, 49(1), 121–138. https://doi.org/10.31341/jios.49.1.8 [Google Scholar] [Crossref]

9. Badmus, E. O., Odekunle, O. P., & Oyewobi, D. O. (2021). Smart fingerprint biometric and RFID time-based attendance management system. European Journal of Electrical Engineering & Computer Science, 5(4).. [Google Scholar] [Crossref]

10. Chukwuemeka, K., Okafor, P., & Nwosu, I. (2020). Remote biometric attendance via mobile apps. International Journal of Mobile Computing, 13(1), 22–30. [Google Scholar] [Crossref]

11. Agalya, G., Srinivasan, R., Vaidianathan, B., & Maria Christy, V. (2025). Cloud-based digital attendance tracker: A paperless and error-free solution for student attendance management. Procedia of Engineering and Medical Sciences, 10(1). [Google Scholar] [Crossref]

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