Main Article Content
The article emphasizes the significance of attendance monitoring for safety during the COVID-19 pandemic and proposes an RFID-based solution coupled with face mask detection systems to address attendance challenges. The project aims to create a contactless monitoring system that ensures face mask compliance and provides real-time attendance data for data-driven decision-making. The article also covers various technology-related topics, including the historical usage of face masks, the development of attendance systems using biometric identification and electronic methods, and facial recognition technology's applications in surveillance and finance. It introduces XAMPP, a user-friendly web application development and testing tool, and presents an overview of the IC7408 chip used in digital electronics. The study's key findings show that increasing sample size and optimizing epochs and batch size improve face mask detection accuracy, while RFID scanner distance affects scanning delay and accuracy. The research provides valuable insights into the performance of the proposed attendance monitoring system.
(Manuscript received: 23 June 2023 | Accepted: 10 August 2023 | Published: 30 September 2023)
B. J. Strasser and T. Schlich, “A history of the medical mask and the rise of throwaway culture,” The Lancet, vol. 396, no. 10243, pp. 19–20, Jul. 2020, doi: 10.1016/S0140-6736(20)31207-1.
A. Singh Upreti et al., “Attendance Monitoring System Using Face Recognition,” International Journal of Information Technology, vol. 1, no. 3, p. 5343, 2022, doi: 10.5281/zenodo.7385439.
I. Adjabi, A. Ouahabi, A. Benzaoui, and A. Taleb-Ahmed, “Past, present, and future of face recognition: A review,” Electronics (Switzerland), vol. 9, no. 8. MDPI AG, pp. 1–53, Aug. 01, 2020. doi: 10.3390/electronics9081188.
Vibhuti, N. Jindal, H. Singh, and P. S. Rana, “Face mask detection in COVID-19: a strategic review,” Multimed Tools Appl, vol. 81, no. 28, pp. 40013–40042, Nov. 2022, doi: 10.1007/s11042-022-12999-6.
M. Z. Asghar et al., “Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic,” Front Public Health, vol. 10, Mar. 2022, doi: 10.3389/fpubh.2022.855254.
O. Tayo Arulogun, A. Olatunbosun, F. O. A, and O. Mikail Olaniyi, “RFID-Based Students Attendance Management System Development of asphalt paved road pothole detection system using modified colour space approach View project Smart and Secure Tele-Clinic diagnostic Systems View project,” 2013. [Online]. Available: http://www.ijser.org
Sarah Amsler and Sharon Shea, “What is RFID and how does it work?,” 2021. https://www.techtarget.com/iotagenda/definition/RFID-radio-frequency-identification (accessed Apr. 23, 2023).
C. A. Ramezan, T. A. Warner, A. E. Maxwell, and B. S. Price, “Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data,” Remote Sens (Basel), vol. 13, no. 3, pp. 1–27, Feb. 2021, doi: 10.3390/rs13030368.
P. M. Radiuk, “Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets,” Information Technology and Management Science, vol. 20, no. 1, Jan. 2018, doi: 10.1515/itms-2017-0003.
N. M. Athif, F. Sthevanie, and K. N. Ramadhan, “FACE MASK DETECTION UNDER LOW LIGHT CONDITION USING CONVOLUTIONAL NEURAL NETWORK (CNN),” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 8, no. 1, pp. 281–290, Feb. 2023, doi: 10.29100/jipi.v8i1.3324.