Manuscript received July 5, 2022; revised August 4, 2022; accepted February 16, 2023.
Abstract—In the ever-expanding world of technological
advancement and development, many new concepts and
technologies are being presented to the world that aim to
streamline current processes or innovate an approach to an
existing problem. The pandemic has brought forward new
operating conditions that have forced many to make the leap to
go fully digital, including the education sector. Therefore, this
conducted study aims to develop facial recognition attendance
system that promotes convenience and accurate attendance
marking. This is crucial to ensure time taken to record students’
attendance could be minimize without any sacrifice in accuracy,
and theoretically limit the occurrence of proxy attendance
within the education setting. Quantitative research was
conducted in Malaysia, which involved 82 students from private
university. This is crucial to ensure students are able to engage
in the teaching and learning process, and at the same time
develop a meaningful learning environment. Data were
analyzed using the Statistical Package for the Social Sciences
(SPSS) software. Research findings show that the facial
recognition attendance system contribute to improve the quality
of education within online settings by limiting the occurrence of
attendance proxying among students. Therefore, this finding
contributes by providing a research direction for improving
attendance in education setting by utilizing facial recognition
attendance system.
Index Terms—Facial recognition, computer vision,
attendance system, education
The authors are with the School of Technology, Asia Pacific University of
Technology and Innovation (APU), Malaysia.
*Correspondence: jkstria@gmail.com (L.J.S.)
Cite: Luthfi Jaka Satria*, Intan Farahana Kamsin, and Nur Khairunnisha Zainal, "Face-Nest, Facial Recognition Attendance System with Timestamp Logs: An Information System Security Approach," International Journal of Information and Education Technology vol. 13, no. 8, pp. 1304-1312, 2023.
Copyright © 2023 by the authors. This 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 (CC BY 4.0).