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Abstract
Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the “nano” version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/ACCESS.2023.3272214
- License
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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IEEE Access
no. 11,
pages 43349 - 43359,
ISSN: 2169-3536 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Kowalczyk N., Sobotka M., Rumiński J.: Mask Detection and Classification in Thermal Face Images// IEEE Access -Vol. 11, (2023), s.43349-43359
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/access.2023.3272214
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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