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Masked face recognition with convolutional neural networks and local binary patterns
Face recognition is one of the most common biometric authentication methods as its feasibility while convenient use. Recently, the COVID-19 pandemic is dramatically spreading throughout the world, which seriously leads to negative impacts on people’s health and economy. Wearing masks in public setti...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8363871/ https://www.ncbi.nlm.nih.gov/pubmed/34764616 http://dx.doi.org/10.1007/s10489-021-02728-1 |
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author | Vu, Hoai Nam Nguyen, Mai Huong Pham, Cuong |
author_facet | Vu, Hoai Nam Nguyen, Mai Huong Pham, Cuong |
author_sort | Vu, Hoai Nam |
collection | PubMed |
description | Face recognition is one of the most common biometric authentication methods as its feasibility while convenient use. Recently, the COVID-19 pandemic is dramatically spreading throughout the world, which seriously leads to negative impacts on people’s health and economy. Wearing masks in public settings is an effective way to prevent viruses from spreading. However, masked face recognition is a highly challenging task due to the lack of facial feature information. In this paper, we propose a method that takes advantage of the combination of deep learning and Local Binary Pattern (LBP) features to recognize the masked face by utilizing RetinaFace, a joint extra-supervised and self-supervised multi-task learning face detector that can deal with various scales of faces, as a fast yet effective encoder. In addition, we extract local binary pattern features from masked face’s eye, forehead and eyebow areas and combine them with features learnt from RetinaFace into a unified framework for recognizing masked faces. In addition, we collected a dataset named COMASK20 from 300 subjects at our institution. In the experiment, we compared our proposed system with several state of the art face recognition methods on the published Essex dataset and our self-collected dataset COMASK20. With the recognition results of 87% f1-score on the COMASK20 dataset and 98% f1-score on the Essex dataset, these demonstrated that our proposed system outperforms Dlib and InsightFace, which has shown the effectiveness and suitability of the proposed method. The COMASK20 dataset is available on https://github.com/tuminguyen/COMASK20 for research purposes. |
format | Online Article Text |
id | pubmed-8363871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-83638712021-08-15 Masked face recognition with convolutional neural networks and local binary patterns Vu, Hoai Nam Nguyen, Mai Huong Pham, Cuong Appl Intell (Dordr) Article Face recognition is one of the most common biometric authentication methods as its feasibility while convenient use. Recently, the COVID-19 pandemic is dramatically spreading throughout the world, which seriously leads to negative impacts on people’s health and economy. Wearing masks in public settings is an effective way to prevent viruses from spreading. However, masked face recognition is a highly challenging task due to the lack of facial feature information. In this paper, we propose a method that takes advantage of the combination of deep learning and Local Binary Pattern (LBP) features to recognize the masked face by utilizing RetinaFace, a joint extra-supervised and self-supervised multi-task learning face detector that can deal with various scales of faces, as a fast yet effective encoder. In addition, we extract local binary pattern features from masked face’s eye, forehead and eyebow areas and combine them with features learnt from RetinaFace into a unified framework for recognizing masked faces. In addition, we collected a dataset named COMASK20 from 300 subjects at our institution. In the experiment, we compared our proposed system with several state of the art face recognition methods on the published Essex dataset and our self-collected dataset COMASK20. With the recognition results of 87% f1-score on the COMASK20 dataset and 98% f1-score on the Essex dataset, these demonstrated that our proposed system outperforms Dlib and InsightFace, which has shown the effectiveness and suitability of the proposed method. The COMASK20 dataset is available on https://github.com/tuminguyen/COMASK20 for research purposes. Springer US 2021-08-14 2022 /pmc/articles/PMC8363871/ /pubmed/34764616 http://dx.doi.org/10.1007/s10489-021-02728-1 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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 | Article Vu, Hoai Nam Nguyen, Mai Huong Pham, Cuong Masked face recognition with convolutional neural networks and local binary patterns |
title | Masked face recognition with convolutional neural networks and local binary patterns |
title_full | Masked face recognition with convolutional neural networks and local binary patterns |
title_fullStr | Masked face recognition with convolutional neural networks and local binary patterns |
title_full_unstemmed | Masked face recognition with convolutional neural networks and local binary patterns |
title_short | Masked face recognition with convolutional neural networks and local binary patterns |
title_sort | masked face recognition with convolutional neural networks and local binary patterns |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8363871/ https://www.ncbi.nlm.nih.gov/pubmed/34764616 http://dx.doi.org/10.1007/s10489-021-02728-1 |
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