The development of a face recognition-based mobile application for student attendance recording
DOI:
https://doi.org/10.37134/jictie.vol10.1.4.2023Keywords:
attendance-taking, face recognition, post-CovidAbstract
Attendance-taking has been practised in most learning institutions, schools, colleges, and universities to monitor the students’ commitment towards their studies. The traditional way of attendance-taking is usually performed by passing a piece of paper to be signed by all the students in the class. The main drawback is proxy attendance, whereby a student can sign on behalf of their friend. Students frequently miss classes are at a higher risk of missing important information, discussions, demonstrations, and assignments, leading to knowledge gaps and hindering their overall academic progress. Additionally, traditional attendance-taking takes significant time for the paper to circulate among the students, especially in larger classrooms. This can result in wasted instructional time and disrupt the class flow. The students might be distracted, and their focus on the class material might also be disrupted. Regarding record management, paper-based attendance records can easily get misplaced, damaged, or lost. Retrieving and analysing attendance data becomes time-consuming when papers must be manually sorted. Not only that, considering the post-Covid pandemic nowadays, everyone is still expected to minimise physical contact to reduce the possibility of infection. This, however, is quite impossible if manual attendance-taking is practised because everyone needs to touch the same paper to sign the attendance. Therefore, in this project, a face recognition-based mobile application is proposed to facilitate attendance-taking while minimising the drawbacks of the traditional attendance-taking approach. The main content of this article will be based on the phases involved in the development process. The output interface of the proposed system will be presented from both lecturers’ and students’ views. Finally, this paper is ended with the system limitations and recommendations for future work.
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Dev, S. and Patnaik, T. (2020). Student attendance system using face recognition. International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2020, 90-96. https://doi.org/10.1109/ICOSEC49089.2020.9215441.
Eclipse Foundation. (2022). Eclipse Downloads | The Eclipse Foundation. https://www.eclipse.org/downloads/
Gawande, U., Joshi, P., Ghatwai, S., Nemade, S., Balkothe, S., Shrikhande, N. (2022). Efficient Attendance Management System Based on Face Recognition. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Lecture Notes in Networks and Systems, vol 321. Springer, Singapore. https://doi.org/10.1007/978-981-16-5987-4_12
Ghimire, A., Werghi, N., Javed, S., & Dias, J. (2022). Real-Time Face Recognition System. arXiv preprint arXiv:2204.08978.
Google. (2021). Face Detection | ML Kit - Google Developers. https://developers.google.com/ml-kit/vision/face-detection
Google. (2022). Flutter - Build apps for any screen. https://flutter.dev
Google & Jetbrains. (2022). Download Android Studio & App Tools - Android Developers. https://developer.android.com/studio
Hoo, S. C. & Ibrahim, H. Review article biometric-based attendance tracking system for education sectors: A literature survey on hardware requirements. Hindawi Journal of Sensors, 2019, 1-25.
Isinkaye, F. O. (2020). An android-based face recognition system for class attendance and malpractice control. International Journal of Computer Science and Information Security (IJCSIS), 18(1), 79-83.
Jin, B., Cruz, L., & Gonçalves, N. (2020). Deep facial diagnosis: deep transfer learning from face recognition to facial diagnosis. IEEE Access, 8, 123649-123661.
Martínez-Díaz, Y., Méndez-Vázquez, H., Luevano, L. S., Nicolás-Díaz, M., Chang, L., & González-Mendoza, M. (2021). Towards accurate and lightweight masked face recognition: an experimental evaluation. IEEE Access, 10, 7341-7353.
Microsoft. (2022). Xamarin Free. Cross-platform. Open source. An app platform for building Android and iOS apps with .NET and C#. https://dotnet.microsoft.com/en-us/apps/Xamarin
Microsoft Corporation. (2022). Visual Studio: IDE and Code Editor for Software Developers. https://visualstudio.microsoft.com/
Qi, S., Zuo, X., Feng, W., & Naveen, I. G. (2022, December). Face Recognition Model Based On MTCNN And Facenet. In 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-5). IEEE.
Shoewu, O., Makanjuola, N.T. & Olatinwo, S.O. (2014). Biometric-based Attendance System: LASU Epe Campus as Case Study. American Journal of Computing Research Repository. 2(1), 8-14. https://doi.org/10.12691/ajcrr-2-1-2.
Tamplin, J. & Lee, A. (2011). Firebase Realtime Database. https://firebase.google.com
Tee, F.K. et al. (2022). JomFacial Recognition Attendance Systems. In: Al-Emran, M., Al-Sharafi, M.A., Al-Kabi, M.N., Shaalan, K. (eds). Proceedings of International Conference on Emerging Technologies and Intelligent Systems. ICETIS 2021. Lecture Notes in Networks and Systems, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-030-82616-1_55
William, I., Rachmawanto, E. H., Santoso, H. A., & Sari, C. A. (2019, October). Face recognition using facenet (survey, performance test, and comparison). In 2019 fourth International Conference on Informatics and Computing (ICIC) (pp. 1-6). IEEE.
Van Casteren, W. (2017). The Waterfall Model and Agile Methodologies: A comparison by project characteristics. 10–13. https://doi.org/10.13140/RG.2.2.10021.50403.
Yadav, A.R., Kumar, J., Anumeha, Agrawal, A.K. & Kumar, R. (2022). Contactless attendance system: A health- care approach to prevent spreading of Covid. Blockchain for 5G Healthcare Applications: Security and privacy solutions: 347-374. https://doi.org/10.1049/PBHE035E.
Zhao, Y., Yu, A. & Xu, D. Person Recognition Based on FaceNet under Simulated Prosthetic Vision Person Recognition Based on FaceNet under Simulated Prosthetic Vision. Journal of Physics: Conference Series, 1437 012012, 1–8. https://doi.org/10.1088/1742-6596/1437/1/012012.
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Copyright (c) 2023 Wan Fariza Wan Abdul Rahman, Nur Athirah Syafiqah Roslan
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