Cargando…

Efficient masked face recognition method during the COVID-19 pandemic

The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face ar...

Descripción completa

Detalles Bibliográficos
Autor principal: Hariri, Walid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8591434/
https://www.ncbi.nlm.nih.gov/pubmed/34804243
http://dx.doi.org/10.1007/s11760-021-02050-w
_version_ 1784599233667530752
author Hariri, Walid
author_facet Hariri, Walid
author_sort Hariri, Walid
collection PubMed
description The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN), namely VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance compared to other state-of-the-art methods.
format Online
Article
Text
id pubmed-8591434
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Springer London
record_format MEDLINE/PubMed
spelling pubmed-85914342021-11-15 Efficient masked face recognition method during the COVID-19 pandemic Hariri, Walid Signal Image Video Process Original Article The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN), namely VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance compared to other state-of-the-art methods. Springer London 2021-11-15 2022 /pmc/articles/PMC8591434/ /pubmed/34804243 http://dx.doi.org/10.1007/s11760-021-02050-w Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Hariri, Walid
Efficient masked face recognition method during the COVID-19 pandemic
title Efficient masked face recognition method during the COVID-19 pandemic
title_full Efficient masked face recognition method during the COVID-19 pandemic
title_fullStr Efficient masked face recognition method during the COVID-19 pandemic
title_full_unstemmed Efficient masked face recognition method during the COVID-19 pandemic
title_short Efficient masked face recognition method during the COVID-19 pandemic
title_sort efficient masked face recognition method during the covid-19 pandemic
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8591434/
https://www.ncbi.nlm.nih.gov/pubmed/34804243
http://dx.doi.org/10.1007/s11760-021-02050-w
work_keys_str_mv AT haririwalid efficientmaskedfacerecognitionmethodduringthecovid19pandemic