DCS-Gait: A Class-Level Domain Adaptation Approach for Cross-Scene and Cross-State Gait Recognition Using Wi-Fi CSI

Liang, Y; Wu, W; Li, H; Chang, X; Chen, X; Peng, J; Xu, P

DCS-Gait: A Class-Level Domain Adaptation Approach for Cross-Scene and Cross-State Gait Recognition Using Wi-Fi CSI

Liang, Y Wu, W Li, H Chang, X

Chen, X Peng, J Xu, P

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2024, 19, pp. 2997-3007
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Liang, Y
dc.contributor.author	Wu, W
dc.contributor.author	Li, H
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Chen, X
dc.contributor.author	Peng, J
dc.contributor.author	Xu, P
dc.date.accessioned	2024-08-07T04:43:34Z
dc.date.available	2024-08-07T04:43:34Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2024, 19, pp. 2997-3007
dc.identifier.issn	1556-6013
dc.identifier.issn	1556-6021
dc.identifier.uri	http://hdl.handle.net/10453/180224
dc.description.abstract	Wi-Fi CSI-based gait recognition is a non-intrusive passive biometric identification technology that has garnered significant attention in the fields of security and smart furniture due to its user-friendly nature. However, in practical application scenarios, gait recognition systems face the challenge of reliably identifying subjects across different scenes or states. To overcome this challenge, this paper proposes DCS-Gait, a domain adaptation solution for cross-scene and cross-state gait recognition based on Wi-Fi CSI. DCS-Gait leverages a novel data distribution measurement called Cross-Attention Metric to align the class-level data distribution differences, enabling the model to learn invariant features across scenes and states. To address the issue of data annotation, we employ a pre-training method to obtain pseudo labels for the dataset. Additionally, a combined matching filtering technique is utilized to generate high-quality pseudo labels for unrecognized data, which can be further employed for supervised model training. We evaluated the effectiveness of DCS-Gait on a large test set consisting of 34 subjects, 2 scenes, and 3 different states, and the results demonstrate significant improvements over the state-of-the-art baselines in both cross-scene and cross-state gait recognition tasks. DCS-Gait provides a promising and reliable solution for accurate cross-scene and cross-state gait recognition in real-world settings.
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.2024.3356827
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	DCS-Gait: A Class-Level Domain Adaptation Approach for Cross-Scene and Cross-State Gait Recognition Using Wi-Fi CSI
dc.type	Journal Article
utslib.citation.volume	19
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T04:43:32Z
pubs.publication-status	Published
pubs.volume	19

Abstract:

Wi-Fi CSI-based gait recognition is a non-intrusive passive biometric identification technology that has garnered significant attention in the fields of security and smart furniture due to its user-friendly nature. However, in practical application scenarios, gait recognition systems face the challenge of reliably identifying subjects across different scenes or states. To overcome this challenge, this paper proposes DCS-Gait, a domain adaptation solution for cross-scene and cross-state gait recognition based on Wi-Fi CSI. DCS-Gait leverages a novel data distribution measurement called Cross-Attention Metric to align the class-level data distribution differences, enabling the model to learn invariant features across scenes and states. To address the issue of data annotation, we employ a pre-training method to obtain pseudo labels for the dataset. Additionally, a combined matching filtering technique is utilized to generate high-quality pseudo labels for unrecognized data, which can be further employed for supervised model training. We evaluated the effectiveness of DCS-Gait on a large test set consisting of 34 subjects, 2 scenes, and 3 different states, and the results demonstrate significant improvements over the state-of-the-art baselines in both cross-scene and cross-state gait recognition tasks. DCS-Gait provides a promising and reliable solution for accurate cross-scene and cross-state gait recognition in real-world settings.

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http://hdl.handle.net/10453/180224