Smart System to Recapitulate Student Attendance on Virtual Meeting Platforms During Covid-19

Authors

DOI:

https://doi.org/10.3991/ijim.v17i11.36479

Keywords:

Facial Recognition, Haar Cascade, MTCNN, FaceNet, ResNet

Abstract


Educators have problems conducting online learning, such as monitoring student attendance while presenting the material. This paper aims to predict student names who attend zoom video conferences with various lighting conditions and face angles by comparing two detection and two recognition methods. This paper proposes an intelligent system based on the use of a bot that will analyse a combination of face detection and recognition method for attendance systems using video conferencing applications to carry out online learning. The proposed system will use the best combination of two methods to recapitulate student attendance. The face detection system uses Haar Cascade and MTCNN, and the face recognition system uses ResNet and FaceNet. The tests were conducted on video zoom footage taken during online lectures. The results show that MTCNN and FaceNet get the highest accuracy, 93.23%.

Cover iJIM Vol. 17 No. 11 (2023)

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Published

2023-06-07

How to Cite

Rahmad, C., Rachmad Syulistyo , A. ., R.H. Putra, D., Prati, A., Fontanini, T., & Ariyanto, R. (2023). Smart System to Recapitulate Student Attendance on Virtual Meeting Platforms During Covid-19. International Journal of Interactive Mobile Technologies (iJIM), 17(11), pp. 171–178. https://doi.org/10.3991/ijim.v17i11.36479

Issue

Vol. 17 No. 11 (2023)

Section

Short Papers

License

Copyright (c) 2023 Cahya Rahmad, Arie Rachmad Syulistyo , Dimas R.H. Putra, Andrea Prati; Tomaso Fontanini, Rudy Ariyanto

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