FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases
Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-fac...
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Published in | PloS one Vol. 19; no. 6; p. e0288670 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
13.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Current address: Department of Environment, School of Geography, Centre of Data Analysis and Policy, University of Leeds, Leeds, Unted Kingdom |
ISSN: | 1932-6203 1932-6203 |
DOI: | 10.1371/journal.pone.0288670 |