research-article

The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition

Authors:

Haruyuki Iwama,

Mayu Okumura,

Yasushi Makihara,

Yasushi YagiAuthors Info & Claims

IEEE Transactions on Information Forensics and Security, Volume 7, Issue 5

Pages 1511 - 1521

https://doi.org/10.1109/TIFS.2012.2204253

Published: 01 October 2012 Publication History

Abstract

This paper describes the world's largest gait database—the “OU-ISIR Gait Database, Large Population Dataset”—and its application to a statistically reliable performance evaluation of vision-based gait recognition. Whereas existing gait databases include at most 185 subjects, we construct a larger gait database that includes 4007 subjects (2135 males and 1872 females) with ages ranging from 1 to 94 years. The dataset allows us to determine statistically significant performance differences between currently proposed gait features. In addition, the dependences of gait-recognition performance on gender and age group are investigated and the results provide several novel insights, such as the gradual change in recognition performance with human growth.

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Hou SHu XCao CLiu XHuang Y(2024)Adaptive Knowledge Transfer for Weak-Shot Gait RecognitionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.342837119(7290-7303)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3428371
Hou SHuang PLiu XCao CHuang Y(2024)Cloth-Imbalanced Gait Recognition via HallucinationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.336023234:7(5665-5676)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3360232
Güner Şahan PŞahin SKaya Gülağız F(2024)A survey of appearance-based approaches for human gait recognition: techniques, challenges, and future directionsThe Journal of Supercomputing10.1007/s11227-024-06172-z80:13(18392-18429)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06172-z
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The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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IEEE Transactions on Information Forensics and Security Volume 7, Issue 5

October 2012

243 pages

ISSN:1556-6013

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IEEE Press

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Published: 01 October 2012

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Hou SHu XCao CLiu XHuang Y(2024)Adaptive Knowledge Transfer for Weak-Shot Gait RecognitionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.342837119(7290-7303)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3428371
Hou SHuang PLiu XCao CHuang Y(2024)Cloth-Imbalanced Gait Recognition via HallucinationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.336023234:7(5665-5676)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3360232
Güner Şahan PŞahin SKaya Gülağız F(2024)A survey of appearance-based approaches for human gait recognition: techniques, challenges, and future directionsThe Journal of Supercomputing10.1007/s11227-024-06172-z80:13(18392-18429)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06172-z
Peng YMa KZhang YHe Z(2024)Learning rich features for gait recognition by integrating skeletons and silhouettesMultimedia Tools and Applications10.1007/s11042-023-15483-x83:3(7273-7294)Online publication date: 1-Jan-2024
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Yang WChen XJi BZhu FChen R(2024)Deep Capsule Network Design Method for Gait RecognitionAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5594-3_6(59-71)Online publication date: 5-Aug-2024
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Guo YDegraeve RRoussel PKaczer BBury EVerbauwhede I(2024)Reducing Reservoir Dimensionality with Phase Space Construction for Simplified Hardware ImplementationArtificial Neural Networks and Machine Learning – ICANN 202410.1007/978-3-031-72359-9_12(156-167)Online publication date: 17-Sep-2024
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Zheng JLiu XWang SWang LYan CLiu WEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Parsing is All You Need for Accurate Gait Recognition in the WildProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612052(116-124)Online publication date: 26-Oct-2023
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Zhang PDou HZhang WZhao YQin ZHu DFang YLi X(2023)A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation and Comparison StudyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/351719919:1(1-23)Online publication date: 5-Jan-2023
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Khan MAzam HFarid M(2023)Automatic multi-gait recognition using pedestrian’s spatiotemporal featuresThe Journal of Supercomputing10.1007/s11227-023-05391-079:17(19254-19276)Online publication date: 26-May-2023
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Parashar AParashar ADing WShekhawat RRida I(2023)Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive reviewArtificial Intelligence Review10.1007/s10462-022-10365-456:8(8889-8953)Online publication date: 18-Jan-2023
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Research on Gait Recognition Based on Lower Limb EMG Signal

The robotic gait simulator: a dynamic cadaveric foot and ankle model for biomechanics research

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