A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition
Abstract
Gait recognition has gained increasing interest from researchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait database, a large set of well designed experiments and some evaluation metrics. There are 124 subjects in the database, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. The database is one of the largest database among the existing databases. Three sets of experiments, including a total of 363 experiments, are designed in the framework. Some metrics are proposed to evaluate gait recognition algorithms
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Wang and Yan used LSTM for cross-view gait recognition based on frame-by-frame GEIs, and experiments on CASIA B and OU-ISIR demonstrate robustness over baselines.
“Concerning Recurrent Neural Networks [43], Wang and Yan [115] used the Long short-term memory [32], a popular RNN architecture capable of learning long-term dependencies, for crossview human gait recognition based on frame-by-frame GEIs. Experiments conducted over CASIA B [128] and OU-ISIR [59] Large Population datasets demonstrate the model's robustness over several baselines.”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [128]
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Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., & Rigoll, G. (2014) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2013.02.006 · Cited by 246 · see citing works
Robust gait recognition: a comprehensive survey
Rida, I., Almaadeed, N., & Almaadeed, S. (2019) · IET Biometrics · doi:10.1049/iet-bmt.2018.5063 · Cited by 119 · see citing works
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 · see citing works
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
Hu, B., Guan, Y., Gao, Y., Long, Y., Lane, N., & Ploetz, T. (2018) · arXiv · doi:10.48550/arxiv.1811.10493 · Cited by 16 · see citing works
Nonstandard Periodic Gait Energy Image for Gait Recognition and Data Augmentation
Wang, K., Liu, L., Lee, Y., Ding, X., & Lin, J. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-31723-2_17 · Cited by 12 · see citing works
- Authors
- Yu, S., Tan, D., & Tan, T.
- Affiliations
- Chinese Academy of Sciences (CN)
- ORCID
- Venue
- 18th International Conference on Pattern Recognition (ICPR'06)
- Publisher
- IEEE
- Published
- 2006
- Pages
- 441-444
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2104335344
- Cited by
- 1,056
- References
- 0
- Citations by year
- 2026: 55 · 2025: 97 · 2024: 114 · 2023: 100 · 2022: 83 · 2021: 108 · 2020: 58 · 2019: 68 · 2018: 47 · 2017: 58 · 2016: 44 · 2015: 47 · 2014: 39 · 2013: 36 · 2012: 24
- FWCI
- 21.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition (publishedVersion)
- Funders
- Georgia Institute of Technology
- University of California, San Diego
- 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, gait database, clothing variation
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