Robust gait recognition: a comprehensive survey
Open accessAbstract
Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra‐class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state‐of‐the‐art performances on several widely used gait recognition datasets.
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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 [79]
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- Authors
- Rida, I., Almaadeed, N., & Almaadeed, S.
- Affiliations
- Qatar University (QA)
- Department of Computer Science and Engineering Qatar University Doha Qatar
- Corresponding author
- Rida, Imad
- Venue
- IET Biometrics
- Published
- 2018-10-31
- Volume
- 8
- Issue
- 1
- Pages
- 14-28
- ISSN
- 2047-4938 (ISSN-L)
- OpenAlex
- W2888394219
- Cited by
- 119
- References
- 220
- Citations by year
- 2025: 6 · 2024: 15 · 2023: 23 · 2022: 24 · 2021: 25 · 2020: 20 · 2019: 4 · 2018: 2
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- IET Biometrics (publishedVersion)
- HAL (submittedVersion, other-oa)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar Foundation
- Qatar National Research Fund
- 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, biometric identification, intra-class variation, covariate effects, clothing variation, viewpoint variation
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