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Cited by 41Face recognition and analysisAdvanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Face mask detection in COVID-19: a strategic review

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Vibhuti, Jindal, Neeru, Singh, Harpreet, Rana, Prashant Singh·2022·Multimedia Tools and Applications
DOI 10.1007/s11042-022-12999-6PMC PMC9069221
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Abstract

With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. However, manually ensuring whether people are wearing face masks or not in a public area is a cumbersome task. The exigency of monitoring people wearing face masks necessitated building an automatic system. Currently, distinct methods using machine learning and deep learning can be used effectively. In this paper, all the essential requirements for such a model have been reviewed. The need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis. Further, the pros and cons of each method have been analyzed in depth. Subsequently, sources to multiple datasets are mentioned. The several software needed for the implementation are also discussed. And discussions have been organized on the various use cases, limitations, and observations for the system, and the conclusion of this paper with several directions for future research.

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  • This application was particularly important during the COVID-19 pandemic, ensuring that healthcare facilities maintained proper protective measures.

    “This application was particularly important during the COVID-19 pandemic, ensuring that healthcare facilities maintained proper protective measures [53].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [53]

    The Applications, limitations, and observations section says face-mask detection could track healthcare workers’ mask use and alert staff to visitors without masks. This supports its potential role in healthcare facilities during COVID-19, but the paper does not establish that the application actually ensured those facilities maintained protective measures. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Vibhuti, Jindal, N., Singh, H., & Rana, P. S.
Affiliations
Thapar Institute of Engineering & Technology (IN)
Corresponding author
Vibhuti
ORCID
Venue
Multimedia Tools and Applications
Publisher
Springer Science and Business Media LLC
Published
2022-05-05
Volume
81
Issue
28
Pages
40013-40042
ISSN
  • 1380-7501 (ISSN-L)
  • 1573-7721
DOI
10.1007/s11042-022-12999-6
PMC
PMC9069221
OpenAlex
W4229018418
Cited by
41
References
3
Citations by year
2026: 4 · 2025: 5 · 2024: 12 · 2023: 12 · 2022: 8
FWCI
3.03
Type
journal article
Language
en
Open access
Licence
Locations
  • Multimedia Tools and Applications (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords
face mask detection, COVID-19 pandemic, deep learning, machine learning, public health compliance, performance comparison

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