The DOI matches its Crossref record; 215 of the 220 references it cites were found in a registry or an index.
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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“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]
Citations
Cites
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Flagged first, then by citations
5 of the 220 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
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Cited by
Sorted by citations
Deep Gait Recognition: A Survey
Sepas‐Moghaddam, A., & Etemad, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3151865 · Cited by 344
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2985628 · Cited by 305
Gait Recognition Based on Deep Learning: A Survey
Filipi Gonçalves dos Santos, C., Oliveira, D. D. S., A. Passos, L., Gonçalves Pires, R., Felipe Silva Santos, D., Pascotti Valem, L., P. Moreira, T., Cleison S. Santana, M., Roder, M., Paulo Papa, J., et al. (2023) · ACM Computing Surveys · doi:10.1145/3490235 · Cited by 108
A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets, and Challenges
Shen, C., Yu, S., Wang, J., Huang, G. Q., & Wang, L. (2024) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3486345 · Cited by 112
Spatial transformer network on skeleton‐based gait recognition
Zhang, C., Chen, X., Han, G., & Liu, X. (2022) · Expert Syst. J. Knowl. Eng. · doi:10.1111/exsy.13244 · Cited by 100
Metadata
- 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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