Search
Abstract
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.
Citations
-
8
CrossRef
-
0
Web of Science
-
1 1
Scopus
Authors (2)
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s21196387
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
SENSORS
no. 21,
ISSN: 1424-8220 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Głowacka N., Rumiński J.: Face with Mask Detection in Thermal Images Using Deep Neural Networks// SENSORS -Vol. 21,iss. 19 (2021), s.6387-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s21196387
- Sources of funding:
-
- Ministerstwo Nauki i Szkolnictwa Wyższego w ramach grantu 6950/II-KDM/SP/2019
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
seen 128 times