Robust view transformation model for gait recognition
Abstract
Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. Conducted on the CASIA gait dataset, experimental results show that the proposed method outperforms the other existing methods.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The CASIA Gait Database is a collection of four datasets designed for gait recognition purposes.
“The Institute of Automation, Chinese Academy of Sciences (CASIA) provides the CASIA Gait Database [137], a collection of four datasets designed for gait recognition purposes”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [137]
The citation lists haven’t been fetched yet
References 13 · Cited by 301
- Authors
- Zheng, S., Zhang, J., Huang, K., He, R., & Tan, T.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- 2011 18th IEEE International Conference on Image Processing
- Publisher
- IEEE
- Published
- 2011-09
- Pages
- 2073-2076
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2038176251
- Cited by
- 301
- References
- 13
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 23 · 2023: 21 · 2022: 22 · 2021: 30 · 2020: 26 · 2019: 34 · 2018: 29 · 2017: 22 · 2016: 22 · 2015: 22 · 2014: 17 · 2013: 13 · 2012: 5
- FWCI
- 23.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- Funders
- National Natural Science Foundation of China (60723005)
- National Laboratory of Pattern Recognition
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- gait recognition, gait energy image, robust principal component analysis, low-rank subspace, viewing angle variation, intra-class variation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
