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Research Article |

Authentication System Using Biometric Data for Face Recognition

Biometric Attendance System Using Face Recognition initiative offers a game-changing answer to the age-old problem of tracking attendance. This innovative solution uses face recognition technology to automate attendance management, providing accuracy, efficiency, and security. The project, which was created on the Android platform, makes use of the capabilities of Kotlin as the core programming language. TensorFlow, a strong machine learning framework, enhances the system's functionality by assisting in real-time face detection and recognition. Android Studio, a versatile IDE designed for Android app development, was the development environment of choice. A careful data collection strategy that included observation and interviews yielded useful insights into the limits of traditional manual attendance systems. The algorithm performs facial feature extraction, comparison, and matching against the stored biometric data to determine the identity of the individual. To ensure data privacy and security, the system employed advanced encryption techniques to protect the biometric data stored in the database. Additionally, measures are in place to prevent unauthorized access to the system and its sensitive information. The Biometric Attendance System offers several advantages over traditional attendance methods. It eliminates the need for manual recording and reduces the potential for errors or fraudulent practices, resulting in more accurate attendance records. The system provides real-time attendance updates to teachers and administrators, enabling timely intervention for absentees. The automation of attendance processes also saves valuable time.

Biometric, Accuracy, Privacy, Security, Recognition, Face Acceptance Rate, Initiative

Chidi Ukamaka Betrand, Chinazo Juliet Onyema, Mercy Eberechi Benson-Emenike, Douglas Allswell Kelechi. (2023). Authentication System Using Biometric Data for Face Recognition. International Journal of Sustainable Development Research, 9(4), 68-78.

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Smith, K. (2018). “Attendance tracking systems in higher education”. In Education and Information Technologies (pp. 441-459). Springer.
2. Jain, A. K., Ross, A., & Prabhakar, S. (2016). “An introduction to biometric recognition”. IEEE Transactions on Circuits and Systems for Video Technology, 14 (1), 4-20.
3. Yadav, A., & Thakur, P. (2019). “Face recognition-based attendance management system”. In 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 936-939). IEEE.
4. Alavi, S. S., Ngo, D. C. L., & Sayed-Mouchaweh, M. (2018). “A facial recognition based automatic attendance management system”. International Journal of Advanced Computer Science and Applications, 9 (2), 495-503.
5. Bhattacharya, S., Dey, S., Biswas, K., &Saha, S. (2019). “Design and implementation of an automatic attendance system using facial recognition technique.”
6. Jumani, A. K. (2019). “Face Detection and Recognition System for Enhancing Security Measures Using Artificial Intelligence System”. Indian Journal of Science and Technology.
7. Turk, M., & Pentland, A. (1991). “Face recognition using eigenfaces”. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 586-591.
8. Zhang, X., Liu, Y., & Li, C. (2017). “Scalability challenges of facial recognition in educational institutions”. Journal of Computer Science and Education, 38 (4), 345-360.
9. Parkhi, O. M., Vedaldi, A., Zisserman, A., & Jawahar, C. V. (2015). “Deep face recognition”. Proceedings of the British Machine Vision Conference, 1, 1-12.
10. Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). “DeepFace: Closing the gap to human-level performance in face verification”. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 1701-1708.
11. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). “FaceNet: A unified embedding for face recognition and clustering”. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 815-823.
12. Aslantas, V., &DurmazIncel, O. (2014). “An efficient and secure fingerprint-based student attendance system”. Procedia Computer Science, 36, 470-477.
13. Jain, A. K., et al. (2016). “Handbook of biometrics for forensic science”. Springer.
14. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., & Berg, A. C. (2016). SSD: “Single Shot MultiBox Detector”. European Conference on Computer Vision, 21-37.
15. Ahmed, M. H., et al. (2018). Automated attendance system using fingerprint recognition for educational institutions”. International Journal of Computer Applications, 181 (1), 32-36.
16. Kumar, A., et al. (2020). “Development of an automated face recognition-based attendance management system using deep learning”. SN Computer Science, 1 (4), 1-14.
17. Rattani, A., et al. (2017). “Security and privacy in biometric systems: A comprehensive review”. IET Biometrics, 6 (3), 161-170.
18. Park, J., & Lee, S. (2019). “A review of facial recognition technology in school attendance systems: Benefits and challenges”. Journal of Educational Technology Research, 25 (2), 123-140.
19. Chen, Y., Wang, H., & Zhang, L. (2018). “Integrating facial recognition with student engagement tracking: Implications for privacy and ethical considerations”. Educational Technology Ethics Review, 10 (3), 201-220.
20. Wang, Q., & Wu, S. (2020). “Enhancing face recognition in school attendance systems using deep learning algorithms”. Proceedings of the International Conference on Machine Learning and Artificial Intelligence, 45-52.
21. Zhang, W., et al. (2015). “Real-time student attendance management system based on face recognition”. International Journal of Database Theory and Application, 8 (3), 271-280.
22. Patel, R., & Mishra, A. (2019). “A comprehensive review of biometric attendance systems in educational institutions”. International Journal of Educational Technology and Innovation, 15 (1), 67-82.