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Cited by 303Gait Recognition and AnalysisBiometric Identification and SecurityHuman Pose and Action Recognition

A Survey on Gait Recognition

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Changsheng Wan, Li Wang, Vir V. Phoha·2019·ACM Computing Surveys
DOI 10.1145/3230633
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Abstract

Recognizing people by their gait has become more and more popular nowadays due to the following reasons. First, gait recognition can work well remotely. Second, gait recognition can be done from low-resolution videos and with simple instrumentation. Third, gait recognition can be done without the cooperation of individuals. Fourth, gait recognition can work well while other features such as faces and fingerprints are hidden. Finally, gait features are typically difficult to be impersonated. Recent ubiquity of smartphones that capture gait patterns through accelerometers and gyroscope and advances in machine learning have opened new research directions and applications in gait recognition. A timely survey that addresses current advances is missing. In this article, we survey research works in gait recognition. In addition to recognition based on video, we address new modalities, such as recognition based on floor sensors, radars, and accelerometers; new approaches that include machine learning methods; and examine challenges and vulnerabilities in this field. In addition, we propose a set of future research directions. Our review reveals the current state-of-art and can be helpful to both experts and newcomers of gait recognition. Moreover, it lists future works and publicly available databases in gait recognition for researchers.

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  • None of the prior surveys focus on presenting the main advances regarding deep learning-based approaches for gait recognition.

    “However, none of them focus on presenting the main advances regarding deep learning-based approaches for gait recognition.”

    From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [110]

    Section 1.2 (Motivation, p. 89:3) says the earlier surveys it examines missed recent work and neglected neural-network classifiers. Section 6.3.4 describes this paper’s own treatment of deep-learning gait recognition as a brief review. These passages support a gap in the older surveys, but do not establish the categorical claim that none of the prior surveys focused on the main deep-learning advances. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Wan, C., Wang, L., & Phoha, V. V.
Affiliations
  • Southeast University (CN)
  • Syracuse University (US)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2018-08-29
Volume
51
Issue
5
Pages
1-35
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3230633
OpenAlex
W2896515397
Cited by
303
References
2
Citations by year
2026: 14 · 2025: 36 · 2024: 50 · 2023: 55 · 2022: 53 · 2021: 45 · 2020: 30 · 2019: 19 · 2018: 1
FWCI
9.51
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Computing Surveys (publishedVersion)
Funders
  • Natural Science Foundation of Jiangsu Province (BK20161099)
  • National Science Foundation (SaTC1527795)
  • National Natural Science Foundation of China (61101088,71402070)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Keywords
gait recognition, personal identification, low-resolution video, floor sensors, smartphone sensors

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