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Cited by 21Gait Recognition and AnalysisHuman Pose and Action RecognitionBiometric Identification and Security

Gait Recognition Under Different Clothing Conditions Via Deterministic Learning

Open access
Deng, Muqing, Wang, Cong·2024·IEEE/CAA Journal of Automatica Sinica
DOI 10.1109/jas.2018.7511096
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Abstract

In real-world scenarios, the clothing difference constitutes one of the most common covariate factors that can affect the performance of gait recognition systems. This paper proposes a gait recognition method which is invariant to maximum number of clothing conditions. First, four kinds of timevarying silhouette features are selected to capture the spatiotemporal characteristics of gait motion. Second, frame-to-frame gait dynamics underlying different individuals ʼ gait features is effectively modeled by radial basis function ( RBF ) neural networks through deterministic learning. Gait patterns are represented as the gait dynamics underlying time-varying gait features. This kind of dynamics information has little sensitivity to the variance between gait patterns under different clothing conditions. In order to eliminate the effect of clothing differences, the training patterns under different clothing conditions further constitute a uniform training dataset, containing all kinds of gait dynamics under different clothing conditions. A rapid recognition scheme is presented on published gait databases. Extensive experiments demonstrate the efficacy of the proposed method.

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  • Non-template-based methods consider the shape and its attributes as the more relevant characteristics, performing recognition with measurements that reflect its shape.

    “On the other hand, nontemplate based methods consider the shape and its attributes as the more relevant characteristics, i.e., the recognition of the individual is performed with measurements that reflect its shape [16].”

    From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [16]

    Section IV (p. 3) says the method measures silhouette widths and area, supporting the reference to shape-related measurements. But the Introduction (p. 1) distinguishes appearance-based from model-based methods, not template-based from nontemplate-based methods, and the experiments (p. 6) say recognition relies on gait dynamics rather than shape information. The source therefore does not support the claim that nontemplate-based methods treat shape as the more relevant characteristic for recognition. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 28

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  28. [29][29] Yu S, Tan D, Tan T. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proceedings of International Conference on Pattern Recognition. IEEE, 2006. 441-444. <!-- image --> Muqing Deng received the B.E. and Ph.D. degrees from the College of Automation, South China University of Technology, Guangzhou, China, in 2012 and 2017, respectively. He is currently a lecturer at the College of Automation, Hangzhou Dianzi University, Hangzhou. His current research interests include dynamical pattern recognition, gait recognition, deterministic learning theory, pattern-based intelligent control, oscillation fault diagnosis, and cognitive and brain sciences. <!-- image --> Cong Wang received the B.E. and M.E. degrees from Beijing University of Aeronautics and Astronautics, Beijing, China, in 1989 and 1997, respectively, and the Ph.D. degree from the National University of Singapore, Singapore, in 2002. Currently, he is a professor at the College of Automation Science and Engineering, South China University of Technology, Guangzhou, China. He has authored and co-authored over 60 papers in international journals and conferences, and is a co-author of the book Deterministic Learning Theory for Identifica- tion, Recognition and Control (Boca Raton, FL: CRC Press, 2009). His current research interests include dynamical pattern recognition, adaptive NN control/identification, deterministic learning theory, pattern-based intelligent control, oscillation fault diagnosis, and cognitive and brain sciences.

Cited by

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Authors
Deng, M., & Wang, C.
Affiliations
  • Guangdong University of Technology (CN)
  • Shandong University (CN)
ORCID
Venue
IEEE/CAA Journal of Automatica Sinica
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024-06
Volume
11
Issue
6
Pages
1530-1532
ISSN
  • 2329-9266 (ISSN-L)
  • 2329-9274
DOI
10.1109/jas.2018.7511096
OpenAlex
W2801396221
Cited by
21
References
1
Citations by year
2026: 2 · 2025: 1 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 2 · 2020: 5 · 2019: 2
FWCI
0.90
Type
journal article
Language
en
Open access
Locations
IEEE/CAA Journal of Automatica Sinica (publishedVersion)
Funders
National Natural Science Foundation of China (T2341019,61803133)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
Keywords
gait recognition, covariate shift, radial basis function neural network, deterministic learning

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