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Deep learning techniques for detecting and recognizing face masks: A survey

The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, preca...

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Autores principales: Alturki, Rahaf, Alharbi, Maali, AlAnzi, Ftoon, Albahli, Saleh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548692/
https://www.ncbi.nlm.nih.gov/pubmed/36225777
http://dx.doi.org/10.3389/fpubh.2022.955332
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author Alturki, Rahaf
Alharbi, Maali
AlAnzi, Ftoon
Albahli, Saleh
author_facet Alturki, Rahaf
Alharbi, Maali
AlAnzi, Ftoon
Albahli, Saleh
author_sort Alturki, Rahaf
collection PubMed
description The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.
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spelling pubmed-95486922022-10-11 Deep learning techniques for detecting and recognizing face masks: A survey Alturki, Rahaf Alharbi, Maali AlAnzi, Ftoon Albahli, Saleh Front Public Health Public Health The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results. Frontiers Media S.A. 2022-09-26 /pmc/articles/PMC9548692/ /pubmed/36225777 http://dx.doi.org/10.3389/fpubh.2022.955332 Text en Copyright © 2022 Alturki, Alharbi, AlAnzi and Albahli. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Alturki, Rahaf
Alharbi, Maali
AlAnzi, Ftoon
Albahli, Saleh
Deep learning techniques for detecting and recognizing face masks: A survey
title Deep learning techniques for detecting and recognizing face masks: A survey
title_full Deep learning techniques for detecting and recognizing face masks: A survey
title_fullStr Deep learning techniques for detecting and recognizing face masks: A survey
title_full_unstemmed Deep learning techniques for detecting and recognizing face masks: A survey
title_short Deep learning techniques for detecting and recognizing face masks: A survey
title_sort deep learning techniques for detecting and recognizing face masks: a survey
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548692/
https://www.ncbi.nlm.nih.gov/pubmed/36225777
http://dx.doi.org/10.3389/fpubh.2022.955332
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