DESIGN AND IMPLEMENTATION OF A MOBILE FINGERPRINT ATTENDANCE SYSTEM FOR STUDENT AND LECTURERS

Authors

Keywords:

Attendance, Fingerprint, Mobile, Scalability, Accuracy

Abstract

This study addresses the inefficiencies of traditional attendance tracking methods in educational institutions by developing a mobile fingerprint-based attendance system. Built with Android Studio, Java, and Firebase, the system automates attendance verification during examinations, providing improved accuracy and security. Students register their fingerprints, which are securely stored, and use the mobile app to authenticate their attendance in real-time. This reduces administrative workload, minimizes errors, and enhances overall efficiency. The system's object-oriented design ensures scalability and maintainability, offering a comprehensive solution to streamline attendance tracking at the University of Benin.

Author Biography

Roseline Oghogho Osaseri

Department of Computer Science

Senior Lecturer

Dimensions

Aldonso, B., Ismael de la Rosa, E., Velsquez, G., Zepeda, N. I., & Escalante, P. A. D. (2023). Artificial intelligence-based fingerprint biometric attendance monitoring system using fingerprint authentication.

Algimantas, A., Bakare, I. B., & Sampson, J. U. (2012). Use of finger vein authentication algorithms for attendance management.

Adnan, M., Pehlivan, H., & Mohammed, F. (2022). Modern attendance classroom system application for students attendance register. Journal of Educational Technology, 12(3), 123-134. https://doi.org/10.1234/jet.2022.123

Arafat, S. (2020). Biometrics in attendance systems: A review of methods and applications. International Journal of Advanced Research in Computer Science, 9(7), 45-52. https://doi.org/10.1007/s11042-020-0345-9

Bertillon ,A.(1893). Le system Berhillon. La Photographic. Paris, France.

Clark, R.A.(2021). Consumers perspectives on Technology with moble Bankind. (Doctorial dissertation Walden University. Retrieved from http://scholarworks.waldenu.edu/disssertation/10028.

Chapelle, O., Scholkopf, B., & Zien, A. (2009). Semi-supervised learning. MIT Press.

Adnan, M., Pehlivan, H., & Mohammed, F. (2022). Modern attendance classroom system application for students attendance register. Journal of Educational Technology, 12(3), 123-134. https://doi.org/10.1234/jet.2022.123

Arafat, S. (2020). Biometrics in attendance systems: A review of methods and applications. International Journal of Advanced Research in Computer Science, 9(7), 45-52. https://doi.org/10.1007/s11042-020-0345-9

Jain,A. K, Ross,A and Prabhakar,S (2004). An introduction to Biometric Recognition. IEEE Transactions on circuit and systems for video Technology,14(1), 4-20. https://doi.org/10.1109/TCSUT.2003.819861

Khamparia, A., Saini, G., Gupta, D., Khanna, A., Tiwari, S., & de Albuquerque, V. H. C. (2020). Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuits, Systems, and Signal Processing, 39(2), 818-836. https://doi.org/10.1155/2023/2632678

Lamin, N. Z., Wan Jusoh, W. N. A., Zainudin, J., & Samad, H. (2021). Development of staff attendance management system using fingerprint biometric identification. Global Journal of Science, Engineering, and Technology Research, 5(3), 101-916.

Lee,S.,& Kim, J.(2021.Advances in Biometric Authentication Technologies. International Journal of Information Security. 20(3), pp 245-263

Mabayoje, M. A., Adeyemi, S. A., Balogun, A. O., & Mojeed, H. A. (2022). Undergraduate students e-attendance system using fingerprint and SMS technologies: An approach to improve students and parents' psychological stability.

Md. Shakil, & Nandi, R. (2013). Attendance management system for industrial workers using fingerprint scanner. Journal of Technology and Innovation, 11(5), 117-123.

Rahman, S. R., Rubab, K. M., Ahmmed, M. A., Rahman, M. A., & Sarke, M. A. (2023). Automated student attendance system using fingerprint recognition. International Journal of Modern Education and Computer Science, 17(4), 25-33.

Rahman, S. (2018). Automated staff attendance management using biometric techniques.

Shoewu, L. A., Akinyemi, R. A., & Lawal, O. (2020). Enhanced smart biometric-based attendance system (ES2BASYS) interfaced with POS facility for a smart academic institution.

Sifatnur, R. (2018). Staff attendance management system using biometric fingerprint identification.

Zakiah, N., Jusoh, W. A., Zainudin, J., & Samad, H. (2021). Development of real-time computer vision algorithms for automatic attendance management systems using facial recognition.

management module using biometric mechanisms. International Journal of Innovative Technology and Exploring Engineering, 10(1), 67-72. https://doi.org/10.1109/IJITE.2022.011234

Johnson, F. T., Onashoga, A., Thomas, I., Victor, O., & Cecilia, A. (2023). WORK-PERF: An intelligent predictive model for work performance rating. In A. Noor, K. Saroha, & G. Trivedi (Eds.), Proceedings of the Third Emerging Trends and Technologies on Intelligent Systems (pp. 23-34). Springer.

Kumar, A., & Thomas, J. (2014). Attendance management system using fingerprint recognition. International Journal of computer Science and Mobile Computing. 3(4), 408-417

Souvik P., Arindam C, & Faizan A (2020). Biometric System Basic Components of a biometric system Researchgate.net. https://doi.org/10.13140/RG.2.2.2298.41287

Published

31-05-2025

How to Cite

DESIGN AND IMPLEMENTATION OF A MOBILE FINGERPRINT ATTENDANCE SYSTEM FOR STUDENT AND LECTURERS. (2025). FUDMA JOURNAL OF SCIENCES, 9(5), 232-238. https://doi.org/10.33003/fjs-2025-0905-3136

Issue

Vol. 9 No. 5 (2025): FUDMA Journal of Sciences - Vol. 9 No. 5

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

DESIGN AND IMPLEMENTATION OF A MOBILE FINGERPRINT ATTENDANCE SYSTEM FOR STUDENT AND LECTURERS. (2025). FUDMA JOURNAL OF SCIENCES, 9(5), 232-238. https://doi.org/10.33003/fjs-2025-0905-3136