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...
| Main Authors: | , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Public Library of Science
2024
|
| _version_ | 1811139701599371264 |
|---|---|
| author | Ibrahim, MR Lyons, T |
| author_facet | Ibrahim, MR Lyons, T |
| author_sort | Ibrahim, MR |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-09-25T04:10:16Z |
| format | Journal article |
| id | oxford-uuid:44bf562d-1afa-48a2-9320-082731ffbc11 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:10:16Z |
| publishDate | 2024 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:44bf562d-1afa-48a2-9320-082731ffbc112024-06-14T20:05:01ZFaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseasesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:44bf562d-1afa-48a2-9320-082731ffbc11EnglishJisc Publications RouterPublic Library of Science2024Ibrahim, MRLyons, TThrough 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. |
| spellingShingle | Ibrahim, MR Lyons, T FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases |
| title | FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases |
| title_full | FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases |
| title_fullStr | FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases |
| title_full_unstemmed | FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases |
| title_short | FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases |
| title_sort | facetouch detecting hand to face touch with supervised contrastive learning to assist in tracing infectious diseases |
| work_keys_str_mv | AT ibrahimmr facetouchdetectinghandtofacetouchwithsupervisedcontrastivelearningtoassistintracinginfectiousdiseases AT lyonst facetouchdetectinghandtofacetouchwithsupervisedcontrastivelearningtoassistintracinginfectiousdiseases |