Cargando…
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...
Autores principales: | , |
---|---|
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 |
_version_ | 1784582935317315584 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8512205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT głowackanatalia facewithmaskdetectioninthermalimagesusingdeepneuralnetworks AT ruminskijacek facewithmaskdetectioninthermalimagesusingdeepneuralnetworks |