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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, 2022.
Article in English | Scopus | ID: covidwho-1898088
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. © 2022, Ismail Saritas. All rights reserved.
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Collection: Databases of international organizations Database: Scopus Type of study: Randomized controlled trials Language: English Journal: International Journal of Intelligent Systems and Applications in Engineering Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: Scopus Type of study: Randomized controlled trials Language: English Journal: International Journal of Intelligent Systems and Applications in Engineering Year: 2022 Document Type: Article