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Cited by 451Gait Recognition and AnalysisHuman Pose and Action RecognitionFace recognition and analysis

Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition

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Takemura, Noriko, Makihara, Yasushi, Muramatsu, Daigo, Echigo, Tomio, Yagi, Yasushi·2018·IPSJ Transactions on Computer Vision and Applications
DOI 10.1186/s41074-018-0039-6
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Abstract

This paper describes the world’s largest gait database with wide view variation, the “OU-ISIR gait database, multi-view large population dataset (OU-MVLP)”, and its application to a statistically reliable performance evaluation of vision-based cross-view gait recognition. Specifically, we construct a gait dataset that includes 10,307 subjects (5114 males and 5193 females) from 14 view angles ranging 0° −90°, 180° −270°. In addition, we evaluate various approaches to gait recognition which are robust against view angles. By using our dataset, we can fully exploit a state-of-the-art method requiring a large number of training samples, e.g., CNN-based cross-view gait recognition method, and we validate effectiveness of such a family of the methods.

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  • DiGGAN obtained state-of-the-art results over the largest multiview gait dataset (OU-MVLP, >10,000 people) using cross-view approaches.

    “An example of the approach can be observed in the UK's Newcastle University cross-view gait recognition system, the so-called DiGGAN, which obtained state-of-the-art results over the largest multiview gait dataset in the world [99] (comprising more than 10 , 000 people) using cross-view approaches [35].”

    From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [99]

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Cited by

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Authors
Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., & Yagi, Y.
Affiliations
  • The University of Osaka (JP)
  • Osaka Electro-Communication University (JP)
Corresponding author
Takemura, Noriko
ORCID
Venue
IPSJ Transactions on Computer Vision and Applications
Publisher
Springer Science and Business Media LLC
Published
2018-02-20
Volume
10
Issue
1
ISSN
1882-6695 (ISSN-L)
DOI
10.1186/s41074-018-0039-6
OpenAlex
W2788751553
Cited by
451
References
1
Citations by year
2026: 34 · 2025: 73 · 2024: 92 · 2023: 80 · 2022: 65 · 2021: 54 · 2020: 29 · 2019: 20 · 2018: 4
FWCI
10.71
Type
journal article
Language
en
Open access
Licence
Locations
  • IPSJ Transactions on Computer Vision and Applications (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Japan Society for the Promotion of Science (JP15H01693)
  • Core Research for Evolutional Science and Technology (Behavior Understanding based on Intention-Gait Model)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Keywords
gait recognition, cross-view gait recognition, performance evaluation, biometrics

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