Goh, Kah Ong and Law, Check Yee and Tin, Cu Kang and Tee, Connie and Sek, Yong Wee (2023) Attendance management system with half-covered and full-facial recognition feature. In: 9th International Conference on Computational Science and Technology, ICCST 2022, 27 August 2022 through 28 August 2022, Johor Bahru.
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Abstract
Manual way of tracking attendance is inefficient and has vulnerability in protecting personal data. Various attendance management systems have been introduced to replace the manual attendance tracking process. This includes the integration of face recognition technique in the attendance management system. However, following the outbreak of pandemic COVID-19, we are strongly advised to always put on a face mask to protect ourselves and others. This practice has caused problems to existing attendance management system with facial recognition. This is because the mask has covered the essential data that can be measured and extracted by the facial recognition algorithms. To overcome this problem, an attendance management system with half-covered and full-facial recognition feature is proposed. MobileFaceNet model is used to verify the user identity for a real-time attendance check-in. Users are able to take attendance via the application with or without a face mask.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Attendance management system, COVID-19, Face mask, Facial recognition, MobileFaceNets |
| Divisions: | Faculty of Information and Communication Technology |
| Depositing User: | Anis Suraya Nordin |
| Date Deposited: | 17 Oct 2024 08:33 |
| Last Modified: | 17 Oct 2024 08:33 |
| URI: | http://eprints.utem.edu.my/id/eprint/28018 |
| Statistic Details: | View Download Statistic |
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