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COVID-19 Face Mask Classification Using Deep Learning

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Data Science and Emerging Technologies (DaSET 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 165))

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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).

Author information

Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan Kampus Kota Bharu, Lembah Sireh, 15050, Kota Bharu, Kelantan, Malaysia

    Nik Amnah Shahidah Abdul Aziz

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan, Bukit Ilmu, 18500, Machang, Kelantan, Malaysia

    Muhammad Firdaus Mustapha

  3. Department of Information Technology, FH Training Center, 16800, Pasir Puteh, Kelantan, Malaysia

    Siti Haslini Ab Hamid

Authors
  1. Nik Amnah Shahidah Abdul Aziz
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  2. Muhammad Firdaus Mustapha
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  3. Siti Haslini Ab Hamid
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Corresponding author

Correspondence to Muhammad Firdaus Mustapha .

Editor information

Editors and Affiliations

  1. UNITAR Graduate School, UNITAR International University, Selangor, Malaysia

    Yap Bee Wah

  2. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

  3. Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia

    Azlinah Mohamed

  4. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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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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