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Face with Mask Detection in Thermal Images Using Deep Neural Networks

As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature...

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Detalles Bibliográficos
Autores principales: Głowacka, Natalia, Rumiński, Jacek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512205/
https://www.ncbi.nlm.nih.gov/pubmed/34640705
http://dx.doi.org/10.3390/s21196387
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author Głowacka, Natalia
Rumiński, Jacek
author_facet Głowacka, Natalia
Rumiński, Jacek
author_sort Głowacka, Natalia
collection PubMed
description As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus protective face masks. As part of this work, a set of thermal images was prepared containing over 7900 images of faces with and without masks. Selected raw data preprocessing methods were also investigated to analyze their influence on the face detection results. It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. The best model turned out to be the one based on Yolov3 model (mean average precision—mAP, was at least 99.3%, while the precision was at least 66.1%). Inference time of the models selected for evaluation on a small and cheap platform allows them to be used for many applications, especially in apps that promote public health.
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spelling pubmed-85122052021-10-14 Face with Mask Detection in Thermal Images Using Deep Neural Networks Głowacka, Natalia Rumiński, Jacek Sensors (Basel) Article As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus protective face masks. As part of this work, a set of thermal images was prepared containing over 7900 images of faces with and without masks. Selected raw data preprocessing methods were also investigated to analyze their influence on the face detection results. It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. The best model turned out to be the one based on Yolov3 model (mean average precision—mAP, was at least 99.3%, while the precision was at least 66.1%). Inference time of the models selected for evaluation on a small and cheap platform allows them to be used for many applications, especially in apps that promote public health. MDPI 2021-09-24 /pmc/articles/PMC8512205/ /pubmed/34640705 http://dx.doi.org/10.3390/s21196387 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Głowacka, Natalia
Rumiński, Jacek
Face with Mask Detection in Thermal Images Using Deep Neural Networks
title Face with Mask Detection in Thermal Images Using Deep Neural Networks
title_full Face with Mask Detection in Thermal Images Using Deep Neural Networks
title_fullStr Face with Mask Detection in Thermal Images Using Deep Neural Networks
title_full_unstemmed Face with Mask Detection in Thermal Images Using Deep Neural Networks
title_short Face with Mask Detection in Thermal Images Using Deep Neural Networks
title_sort face with mask detection in thermal images using deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512205/
https://www.ncbi.nlm.nih.gov/pubmed/34640705
http://dx.doi.org/10.3390/s21196387
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