Okokpujie, Kennedy O. and Anicho-Okoro, Chiamaka and Okokpujie, Imhade P. and Okesola, Julius-Olatunji (2022) Implementation of an Embedded Masked Face Recognition System using Huskylens System-On-Chip Module. In: IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development, 05-07 April 2022, Lagos, Nigeria.
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Abstract
Globally, Facial recognition systems have been increasingly adopted, by governments, as a viable means of identification and verification in public spaces such as the airport, train stations, and stadiums. However, in the wake of the COVID-19 outbreak, the World Health Organization (WHO) declared that wearing face masks is an essential safety precaution. As a result, current facial recognition systems have difficulties recognizing faces accurately, which motivated this study. This research aims to implement an embedded masked face recognition system using the HuskyLens SoC module to identify people, even while wearing a face mask. The developed method was actualized using the Kendryte K210 chip embedded in the HuskyLens module. This system-on-chip design was integrated with other peripherals using an Arduino Pro-mini board. The results of testing and evaluating the system's performance show that the system's facial recognition accuracy with masked and without masks faces was 90% and 95%, respectively. Implementing this solution in our environment would enable accurate real-time recognition of masked and unmasked faces
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Patricia Nwokealisi |
| Date Deposited: | 27 Nov 2024 16:17 |
| Last Modified: | 27 Nov 2024 16:17 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18624 |
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