research-article

Cross-view gait recognition through ensemble learning

Authors:

Wei Qi YanAuthors Info & Claims

Neural Computing and Applications, Volume 32, Issue 11

Pages 7275 - 7287

https://doi.org/10.1007/s00521-019-04256-z

Published: 01 June 2020 Publication History

Abstract

Gait has been well known as an unobtrusive promising biometric to identify a person from a distance. However, the effectiveness of silhouette-based approaches in gait recognition is diluted due to variations of view angles. In this paper, we put forward a novel and effective method of gait recognition: cross-view gait recognition based on ensemble learning. The proposed method greatly enhances the effectiveness and reduces the sensitivity of gait recognition under various view angles conditions. Furthermore, in this paper we will introduce a novel algorithm based on ensemble learning for combining several gait learners together, which utilizes a well-designed gait feature based on area average distance. Through experimental evaluations on the well-known CASIA gait database and OU-ISIR gait database, our paper demonstrates the advantages of the proposed method in comparison with others. The contribution of this research work is to resolve the multiview angles problem of gait recognition through assembling several gait learners.

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Cited By

Semwal VGupta ALalwani P(2021)An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognitionThe Journal of Supercomputing10.1007/s11227-021-03768-777:11(12256-12279)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11227-021-03768-7
Wen JWang X(2021)Cross-view gait recognition based on residual long short-term memoryMultimedia Tools and Applications10.1007/s11042-021-11107-480:19(28777-28788)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-11107-4
Wang XYan K(2021)Gait classification through CNN-based ensemble learningMultimedia Tools and Applications10.1007/s11042-020-09777-780:1(1565-1581)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s11042-020-09777-7
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Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 32, Issue 11

Jun 2020

1119 pages

ISSN:0941-0643

EISSN:1433-3058

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© Springer-Verlag London Ltd., part of Springer Nature 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2020

Accepted: 09 May 2019

Received: 10 January 2019

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National Natural Science Foundation of China

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Cited By

Semwal VGupta ALalwani P(2021)An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognitionThe Journal of Supercomputing10.1007/s11227-021-03768-777:11(12256-12279)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11227-021-03768-7
Wen JWang X(2021)Cross-view gait recognition based on residual long short-term memoryMultimedia Tools and Applications10.1007/s11042-021-11107-480:19(28777-28788)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-11107-4
Wang XYan K(2021)Gait classification through CNN-based ensemble learningMultimedia Tools and Applications10.1007/s11042-020-09777-780:1(1565-1581)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s11042-020-09777-7
Wang XFeng SYan W(2019)Human Gait Recognition Based on Self-Adaptive Hidden Markov ModelIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2019.295114618:3(963-972)Online publication date: 4-Nov-2019
https://dl.acm.org/doi/10.1109/TCBB.2019.2951146

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