Authors : Nicholas Simeon Dienagha; Okoria Ebiabowei David; Biralatei Fawei

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/3rmjayhb

Scribd : https://tinyurl.com/323up3xc

DOI : https://doi.org/10.38124/ijisrt/26mar1963

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Managing attendance represents a significant and essential operational task within academic institutions. The conventional paper-based attendance taking system consumes substantial amount of time and it is prone to human errors and falsification. Most importantly, a high growth in class size may reduce the quality of class attendance data and making it more difficult to ensure compliance and track students participation. The Facial Recognition Attendance System is a webbased platform designed to record classroom attendance automatically while preventing impersonation. The system integrates biometric face enrollment, real-time facial matching, threshold-based attendance decision logic, and comprehensive event logging for recognizing student attendance in a class. By combining facial biometrics with real-time capture and automated analysis, the system improves the trustworthiness of attendance data, reduces opportunities for fraud, and establishes a digital audit trail suitable for compliance and academic reporting. It supports two main user roles: administrators (who manage students, lecturers, courses, classrooms, semesters, reports, and settings) and lecturers (who start and stop attendance sessions, perform live facial recognition, capture attendance evidence, review records, and export session data).

Keywords : Facial Recognition, Attendance, Manual Attendance, Biometric.

References :

1. Landin, M., & Pérez, J. (2015). Class attendance and academic achievement of pharmacy students in a European University. Currents in Pharmacy Teaching and Learning, 7(1), 78-83. http://dx.doi.org/10.1016/j.cptl.2014.09.013.
2. Lukkarinen, A., Koivukangas, P., & Seppälä, T. (2016). Relationship between class attendance and student performance. Procedia-Social and Behavioral Sciences, 228(16), 341-347. http://dx.doi.org/10.1016/j.sbspro.2016.07.051.
3. Purcell, P. (2007, Septembe). Engineering student attendance at lectures: Effect on examination performance. In International conference on engineering education (pp. 3-7).
4. Chen, Jennjou, and Tsui-Fang Lin. 2008. “Class Attendance and Exam Performance: A Randomized Experiment.” The Journal of Economic Education 39(3): 213–27. http://heldrefpublications.metapress.com/app/home/contribution.asp?referrer=parent&backto=issue,1,10;journal,10,56;linkingpublicationresults,1:119930,1.
5. Rjeib, H. D., Ali, N. S., Al Farawn, A., Al-Sadawi, B., & Alsharqi, H. (2018). Attendance and information system using RFID and web-based application for academic sector. International Journal of Advanced Computer Science and Applications, 9(1).
6. Patel, U. A., & Priya, S. (2014). Development of a student attendance management system using RFID and face recognition: a review. International Journal of Advance Research in Computer Science and Management Studies, 2(8), 109-119.
7. Mekala, V., Vinod, V. M., Manimegalai, M., & Nandhini, K. (2019). Face recognition-based attendance system. International Journal of Innovative Technology and Exploring Engineering, 8(12), 520-525.
8. Raghuwanshi, A., & Swami, P. D. (2017). An automated classroom attendance system using video-based face recognition. In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 719-724). IEEE.
9. Bah, S. M., & Ming, F. (2020). An improved face recognition algorithm and its application in attendance management system. Array, 5, 100014.
10. Salunkhe, A., Pawar, V., Pise, P., Mule, S., Survase, A., Godase, V., & Zambre, S. (2025). A Review on Real-Time RFID-Based Smart Attendance Systems for Efficient Record Management. Advance Research in Analog and Digital Communications, 2(2), 32-46.
11. Ishaq, K., & Bibi, S. (2023). IoT based smart attendance system using RFID: A systematic literature review. arXiv preprint arXiv:2308.02591.
12. Joshi, A., Ahmad, A., Saxena, A., & Juneja, P. (2021). RFID based attendance system. Int. J. Modern Trends Sci. Tech, 7, 40-43.
13. Rahman, S., Rahman, M., & Rahman, M. M. (2018). Automated student attendance system using fingerprint recognition. Edelweiss applied science and technology, 1(2), 90-94.
14. Rahman, M. S., Rumman, K. M., Ahmmed, R., Rahman, A., & Sarker, M. A. (2023). Fingerprint based biometric attendance system. Section A-Research paper of European Chemical Bulletin, 12(S3), 184-190.
15. Nuhi, A., Memeti, A., Imeri, F., & Cico, B. (2020, June). Smart attendance system using qr code. In 2020 9th mediterranean conference on embedded computing (MECO) (pp. 1-4). IEEE.
16. Liew, K. J., & Tan, T. H. (2021, September). QR code-based student attendance system. In 2021 2nd Asia Conference on Computers and Communications (ACCC) (pp. 10-14). IEEE.
17. Elaskari, S., Imran, M., Elaskri, A., & Almasoudi, A. (2021). Using barcode to track student attendance and assets in higher education institutions. Procedia Computer Science, 184, 226-233.
18. Siew, E. S. K., Chong, Z. Y., Sze, S. N., & Hardi, R. (2023). Streamlining attendance management in education: A web-based system combining facial recognition and QR code technology. Journal of Advanced Research in Applied Sciences and Engineering Technology, 33(2), 198-208.
19. Al Sheikh, R., Al-Assami, R., Al-Bahar, M., Al Suhaibani, M., Alsmadi, M., Alshabanah, M., ... & Tayfour, M. F. (2019). Developing and implementing a barcode-based student attendance system. International Research Journal of Engineering and Technology (IRJET) Volume, 6.