Progressive Sub-Domain Information Mining for Single-Source Generalizable Gait Recognition

Wang, Y; Huang, Y; Shan, C; Wang, L

Progressive Sub-Domain Information Mining for Single-Source Generalizable Gait Recognition

Wang, Y Huang, Y

Shan, C Wang, L

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2023, 18, pp. 4787-4799
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318
dc.contributor.author	Shan, C
dc.contributor.author	Wang, L
dc.date.accessioned	2024-06-21T02:01:21Z
dc.date.available	2024-06-21T02:01:21Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2023, 18, pp. 4787-4799
dc.identifier.issn	1556-6013
dc.identifier.issn	1556-6021
dc.identifier.uri	http://hdl.handle.net/10453/179608
dc.description.abstract	Recent years have witnessed the deployment of fully supervised gait recognition. However, due to domain diversity, gait recognition models designed under the fully supervised condition suffer from poor generalization in unseen domains. How to improve the generalization ability of gait recognition models and enhance their performance on unseen domains is unexplored in existing gait recognition approaches. This paper investigates the generalizable gait recognition problem and proposes a Progressive Sub-domain Information Mining (PSIM) framework for single-source generalizable gait recognition. During training, the PSIM can mine sub-domain information from a single large-scale source domain by differentiating gait features extracted from different people through unsupervised clustering. Then, domain information mitigation loss and domain homogenization loss are introduced to regularize those gait features to be domain-insensitive. The above procedures are conducted iteratively until the model converges. The PSIM framework is model-agnostic, which can directly improve the generalization ability of state-of-the-art gait recognition models without significantly increasing the complexity of the model. In experiments, our model-agnostic PSIM framework is adopted on several gait recognition models to show its effectiveness in boosting gait recognition performance for the single-source generalizable gait recognition task.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security
dc.relation.isbasedon	10.1109/TIFS.2023.3298518
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Progressive Sub-Domain Information Mining for Single-Source Generalizable Gait Recognition
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-06-21T02:01:19Z
pubs.publication-status	Published
pubs.volume	18

Abstract:

Recent years have witnessed the deployment of fully supervised gait recognition. However, due to domain diversity, gait recognition models designed under the fully supervised condition suffer from poor generalization in unseen domains. How to improve the generalization ability of gait recognition models and enhance their performance on unseen domains is unexplored in existing gait recognition approaches. This paper investigates the generalizable gait recognition problem and proposes a Progressive Sub-domain Information Mining (PSIM) framework for single-source generalizable gait recognition. During training, the PSIM can mine sub-domain information from a single large-scale source domain by differentiating gait features extracted from different people through unsupervised clustering. Then, domain information mitigation loss and domain homogenization loss are introduced to regularize those gait features to be domain-insensitive. The above procedures are conducted iteratively until the model converges. The PSIM framework is model-agnostic, which can directly improve the generalization ability of state-of-the-art gait recognition models without significantly increasing the complexity of the model. In experiments, our model-agnostic PSIM framework is adopted on several gait recognition models to show its effectiveness in boosting gait recognition performance for the single-source generalizable gait recognition task.

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http://hdl.handle.net/10453/179608