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.
| Published in | International Journal of Sustainable Development Research (Volume 9, Issue 4) |
| DOI | 10.11648/j.ijsdr.20230904.12 |
| Page(s) | 68-78 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Biometric, Accuracy, Privacy, Security, Recognition, Face Acceptance Rate, Initiative
| [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. |
APA Style
Ukamaka Betrand, C., Juliet Onyema, C., Eberechi Benson-Emenike, M., Allswell Kelechi, D. (2023). Authentication System Using Biometric Data for Face Recognition. International Journal of Sustainable Development Research, 9(4), 68-78. https://doi.org/10.11648/j.ijsdr.20230904.12
ACS Style
Ukamaka Betrand, C.; Juliet Onyema, C.; Eberechi Benson-Emenike, M.; Allswell Kelechi, D. Authentication System Using Biometric Data for Face Recognition. Int. J. Sustain. Dev. Res. 2023, 9(4), 68-78. doi: 10.11648/j.ijsdr.20230904.12
AMA Style
Ukamaka Betrand C, Juliet Onyema C, Eberechi Benson-Emenike M, Allswell Kelechi D. Authentication System Using Biometric Data for Face Recognition. Int J Sustain Dev Res. 2023;9(4):68-78. doi: 10.11648/j.ijsdr.20230904.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijsdr.20230904.12,
author = {Chidi Ukamaka Betrand and Chinazo Juliet Onyema and Mercy Eberechi Benson-Emenike and Douglas Allswell Kelechi},
title = {Authentication System Using Biometric Data for Face Recognition},
journal = {International Journal of Sustainable Development Research},
volume = {9},
number = {4},
pages = {68-78},
doi = {10.11648/j.ijsdr.20230904.12},
url = {https://doi.org/10.11648/j.ijsdr.20230904.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsdr.20230904.12},
abstract = {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.
},
year = {2023}
}
TY - JOUR T1 - Authentication System Using Biometric Data for Face Recognition AU - Chidi Ukamaka Betrand AU - Chinazo Juliet Onyema AU - Mercy Eberechi Benson-Emenike AU - Douglas Allswell Kelechi Y1 - 2023/11/17 PY - 2023 N1 - https://doi.org/10.11648/j.ijsdr.20230904.12 DO - 10.11648/j.ijsdr.20230904.12 T2 - International Journal of Sustainable Development Research JF - International Journal of Sustainable Development Research JO - International Journal of Sustainable Development Research SP - 68 EP - 78 PB - Science Publishing Group SN - 2575-1832 UR - https://doi.org/10.11648/j.ijsdr.20230904.12 AB - 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. VL - 9 IS - 4 ER -
Department of Computer Science, School of Information and Communication Technology, Federal University of Technology, Owerri, Nigeria
Information