Unsupervised Domain Adaptation for Disguised-gait-based Person Identification on Micro-Doppler Signatures

Unsupervised Domain Adaptation for Disguised-gait-based Person Identification on Micro-Doppler Signatures

Yang, Yang (Tianjin University)
Yang, Xiaoyi (Tianjin University)
Sakamoto, Takuya (Kyoto University)
Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems)
Li, Beichen (Tianjin University)
Lang, Yue (Hebei University of Technology)

2022

In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably identify the subject's identity using gait data. In this paper, we propose an unsupervised domain adaptation (UDA) model, named Guided Subspace Alignment under the Class-Aware condition (G-SAC), to recognize human subjects based on their disguised gait data by fully exploiting the intrinsic information in gait biometrics. To accomplish this, we employ neighbourhood component analysis (NCA) to create an intrinsic feature subspace from which we can obtain similarities between normal and disguised gaits. With the aid of a proposed constraint for adaptive class-Aware alignment, the class-level discriminative feature representation can be learned guided by this subspace. Our experimental results on a measured micro-Doppler radar dataset demonstrate the effectiveness of our approach. The comparison results with several state-of-The-Art methods indicate that our work provides a promising domain adaptation solution for the concerned problem, even in cases where the disguised pattern differs significantly from the normal gaits. Additionally, we extend our approach to more complex multi-Target domain adaptation (MTDA) challenge and video-based gait recognition tasks, the superior results demonstrate that the proposed model has a great deal of potential for tackling increasingly difficult problems.

Micro-Doppler signatures
gait recognition
radar-based person identification
transfer learning
unsupervised domain adaptation

http://resolver.tudelft.nl/uuid:d38e9937-e7f4-4bb4-b13c-06be1ec042b2

https://doi.org/10.1109/TCSVT.2022.3161515

2023-04-01

1051-8215

IEEE Transactions on Circuits and Systems for Video Technology, 32 (9), 6448-6460

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Yang Yang, Xiaoyi Yang, Takuya Sakamoto, F. Fioranelli, Beichen Li, Yue Lang