Masked Face Detection with Illumination Awareness

Dinh, TH; Doan, QM; Trung, NL; Nguyen, DN; Lin, C-T

Masked Face Detection with Illumination Awareness

Dinh, TH Doan, QM Trung, NL Nguyen, DN Lin, C-T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), 2022, 2022-May, pp. 1-6
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Dinh, TH
dc.contributor.author	Doan, QM
dc.contributor.author	Trung, NL
dc.contributor.author	Nguyen, DN
dc.contributor.author	Lin, C-T
dc.date	2022-05-02
dc.date.accessioned	2023-03-23T04:33:39Z
dc.date.available	2023-03-23T04:33:39Z
dc.date.issued	2022-01-01
dc.identifier.citation	2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), 2022, 2022-May, pp. 1-6
dc.identifier.isbn	9781665472517
dc.identifier.issn	2326-828X
dc.identifier.issn	2326-8301
dc.identifier.uri	http://hdl.handle.net/10453/168201
dc.description.abstract	Mask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)
dc.relation.ispartof	2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)
dc.relation.isbasedon	10.1109/ismict56646.2022.9828233
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Masked Face Detection with Illumination Awareness
dc.type	Conference Proceeding
utslib.citation.volume	2022-May
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-23T04:33:38Z
pubs.finish-date	2022-05-04
pubs.publication-status	Published
pubs.start-date	2022-05-02
pubs.volume	2022-May

Abstract:

Mask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.

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http://hdl.handle.net/10453/168201