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EfficientMask-Net for face authentication in the era of COVID-19 pandemic
Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobile and embedding models with low-memory requiremen...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022166/ https://www.ncbi.nlm.nih.gov/pubmed/35469317 http://dx.doi.org/10.1007/s11760-022-02160-z |
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author | Azouji, Neda Sami, Ashkan Taheri, Mohammad |
author_facet | Azouji, Neda Sami, Ashkan Taheri, Mohammad |
author_sort | Azouji, Neda |
collection | PubMed |
description | Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobile and embedding models with low-memory requirements. The method identifies face mask-wearing conditions in two different schemes: I. Correctly Face Mask (CFM), Incorrectly Face Mask (IFM), and Not Face Mask (NFM) wearing; II. Uncovered Chin IFM, Uncovered Nose IFM, and Uncovered Nose and Mouth IFM. The proposed method can also be helpful to unmask the face for face authentication based on unconstrained 2D facial images in the wild. In this study, deep convolutional neural networks (CNNs) were employed as feature extractors. Then, deep features were fed to a recently proposed large margin piecewise linear (LMPL) classifier. In the experimental study, lightweight and very powerful mobile implementation of CNN models were evaluated, where the novel “EffientNetb0” deep feature extractor with LMPL classifier outperformed well-known end-to-end CNN models, as well as conventional image classification methods. It achieved high accuracies of 99.53 and 99.64% in fulfilling the two mentioned tasks, respectively. |
format | Online Article Text |
id | pubmed-9022166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-90221662022-04-21 EfficientMask-Net for face authentication in the era of COVID-19 pandemic Azouji, Neda Sami, Ashkan Taheri, Mohammad Signal Image Video Process Original Paper Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobile and embedding models with low-memory requirements. The method identifies face mask-wearing conditions in two different schemes: I. Correctly Face Mask (CFM), Incorrectly Face Mask (IFM), and Not Face Mask (NFM) wearing; II. Uncovered Chin IFM, Uncovered Nose IFM, and Uncovered Nose and Mouth IFM. The proposed method can also be helpful to unmask the face for face authentication based on unconstrained 2D facial images in the wild. In this study, deep convolutional neural networks (CNNs) were employed as feature extractors. Then, deep features were fed to a recently proposed large margin piecewise linear (LMPL) classifier. In the experimental study, lightweight and very powerful mobile implementation of CNN models were evaluated, where the novel “EffientNetb0” deep feature extractor with LMPL classifier outperformed well-known end-to-end CNN models, as well as conventional image classification methods. It achieved high accuracies of 99.53 and 99.64% in fulfilling the two mentioned tasks, respectively. Springer London 2022-04-21 2022 /pmc/articles/PMC9022166/ /pubmed/35469317 http://dx.doi.org/10.1007/s11760-022-02160-z Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 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 Paper Azouji, Neda Sami, Ashkan Taheri, Mohammad EfficientMask-Net for face authentication in the era of COVID-19 pandemic |
title | EfficientMask-Net for face authentication in the era of COVID-19 pandemic |
title_full | EfficientMask-Net for face authentication in the era of COVID-19 pandemic |
title_fullStr | EfficientMask-Net for face authentication in the era of COVID-19 pandemic |
title_full_unstemmed | EfficientMask-Net for face authentication in the era of COVID-19 pandemic |
title_short | EfficientMask-Net for face authentication in the era of COVID-19 pandemic |
title_sort | efficientmask-net for face authentication in the era of covid-19 pandemic |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022166/ https://www.ncbi.nlm.nih.gov/pubmed/35469317 http://dx.doi.org/10.1007/s11760-022-02160-z |
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