Abstract
The COVID-19 pandemic has triggered a global health disaster because its virus is spread mainly through minute respiratory droplets from coughing, sneezing, or prolonged close contact between individuals. Consequently, World Health Organization (WHO) urged wearing face masks in public places such as schools, train stations, hospitals, etc., as a precaution against COVID-19. However, it takes work to monitor people in these places manually. Therefore, an automated facial mask detection system is essential for such enforcement. Nevertheless, face detection systems confront issues, such as the use of accessories that obscure the face region, for example, face masks. Even existing detection systems that depend on facial features struggle to obtain good accuracy. Recent advancements in object detection, based on deep learning (DL) models, have shown good performance in identifying objects in images. This work proposed a DL-based approach to develop a face mask detector model to categorize masked and unmasked faces in images and real-time streaming video. The model is trained and evaluated on two different datasets, which are synthetic and real masked face datasets. Experiments on these two datasets showed that the performance accuracy rate of this model is 99% and 89%, respectively.
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Acknowledgement
The Fundamental Research Grant Scheme (FRGS) of the Ministry of Education (MOE) supported this work with grant number 600-IRMI/FRGS 5/3 (234/2019).
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Abdul Aziz, N.A.S., Mustapha, M.F., Ab Hamid, S.H. (2023). COVID-19 Face Mask Classification Using Deep Learning. In: Wah, Y.B., Berry, M.W., Mohamed, A., Al-Jumeily, D. (eds) Data Science and Emerging Technologies. DaSET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-99-0741-0_6
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