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

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

Facial Recognition

Haar Cascade

MTCNN

FaceNet

ResNet

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%.

Rahmad, Cahya

Rachmad Syulistyo , Arie

R.H. Putra, Dimas

Prati, Andrea

Fontanini, Tomaso

Ariyanto, Rudy

International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 11 (2023); pp. 171-178

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2023-06-07

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

https://creativecommons.org/licenses/by/4.0

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eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Non-refereed Article