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COVID-19 Face Mask Detection Using CNN and Transfer Learning

In: Decision Sciences for COVID-19

Author

Listed:
  • Cecilia Ajowho Adenusi

    (Linux Professional Institute, Nigeria Master Affiliate)

  • Olufunke Rebecca Vincent

    (Federal University of Agriculture)

  • Jesufunbi Abodunrin

    (Federal University of Agriculture)

  • Bukola Taibat Adebiyi

    (Federal University of Agriculture)

Abstract

The trauma produced by the COVID-19 sickness, which was proclaimed by the World Health Organization (WHO) in 2020, has impacted the entire world. WHO has recommended several recommendations and precautions to effectively prevent the spread of the deadly disease, including social distance, hand sanitizer, and the use of a face mask or face shield. Most particularly in crowded settings, which is what inspired this investigation into one of the WHO recommended preventive measures, namely the use of a face mask. This research used a Convolutional Neural Network and a Transfer Learning Model to determine whether or not a citizen wears a mask. This suggested model is trained and tested on the Face Masked Dataset, then image augmentation on limited available data for improved training and testing, with a 98 percent accuracy rate during training and testing.

Suggested Citation

  • Cecilia Ajowho Adenusi & Olufunke Rebecca Vincent & Jesufunbi Abodunrin & Bukola Taibat Adebiyi, 2022. "COVID-19 Face Mask Detection Using CNN and Transfer Learning," International Series in Operations Research & Management Science, in: Said Ali Hassan & Ali Wagdy Mohamed & Khalid Abdulaziz Alnowibet (ed.), Decision Sciences for COVID-19, chapter 0, pages 393-405, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-87019-5_22
    DOI: 10.1007/978-3-030-87019-5_22
    as

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