IOATS: an Intelligent Online Attendance Tracking System based on Facial Recognition and Edge Computing

Authors

Keywords:

Edge Computing, Facial Recognition, Attendance Tracking, COVID19, Video Conferencing Platform

Abstract

Since the Coronavirus (COVID19) pandemic, all activities have been held digitally, necessitating the surveillance of guests' real-time attendance. Previously, online attendance included getting the list of attendees, which was inconvenient because many people chose to keep silent or leave the meeting completely. As a result, a technique for collecting attendance using facial recognition that can correctly identify participants who remain online for the duration of the lecture is required. The goal of this work is to develop a system named IOSTS, an intelligent online attendance tracking system, that can track attendance while using minimum bandwidth and maintaining user privacy. This proposed work is based on the concepts of facial recognition and edge computing. The entire utility will be run on the client's PC. From random experiments, it is observed that achieving an accuracy of 98 % in facial recognition. This new approach is a foolproof method of tracking attendance and increasing digital transparency.

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Proposed Attendance Tracking System

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Published

27.05.2022

How to Cite

Parhi, M. ., Roul, A. ., Ghosh, B., & Pati, A. (2022). IOATS: an Intelligent Online Attendance Tracking System based on Facial Recognition and Edge Computing. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 252–259. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1892

Issue

Vol. 10 No. 2 (2022)

Section

Research Article

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Copyright (c) 2022 Manoranjan Parhi, Abhinandan Roul, Bravish Ghosh, Abhilash Pati

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