Fast and robust framework for view-invariant gait recognition

Ning Jia, Chang Tsun Li, Victor Sanchez, Alan Wee-Chung Liew

Research output: Book chapter/Published conference paperConference paper

4 Citations (Scopus)

Abstract

View-invariant gait recognition is one of the major challenges in identifying people through their gait. Many researchers have evaluated view angle transformation techniques, discriminant analysis and manifold learning approaches for cross-view recognition, and their proposals are usually based on a common factor, i.e., to establish a cross-view mapping between gallery and probe templates. However, their effectiveness is restricted to small view angle variances. A promising approach to perform view-invariant gait recognition is through multi-view feature learning. In this paper, we propose the view-invariant feature selector (ViFS) and integrate it in a framework for view-invariant gait recognition. ViFS select features from multi-view gait templates and reconstructs gallery templates that accurately match the data for a specific view angle. ViFS is thus able to reconstruct gallery templates from arbitrary view angles, and thus help to transfer the cross-view problem to identical-view gait recognition. We also apply linear subspace learning methods as feature enhancers for ViFS, which substantially reduce the computational cost and improve the recognition speed. We test the proposed framework on the CASIA Dataset B. The average recognition accuracy of the proposed framework for 11 different views exceed 98%.
Original languageEnglish
Title of host publicationProceedings - 2017 5th International Workshop on Biometrics and Forensics (IWBF 2017)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509057917
DOIs
Publication statusPublished - 29 May 2017
Event5th International Workshop on Biometrics and Forensics: IWBF 2017 - University of Warwick, Coventry, United Kingdom
Duration: 04 Apr 201705 Apr 2017
https://warwick.ac.uk/fac/sci/dcs/people/chang-tsun_li/iwbf2017/ (Conference website)

Conference

Conference5th International Workshop on Biometrics and Forensics
CountryUnited Kingdom
CityCoventry
Period04/04/1705/04/17
OtherIWBF is an international forum devoted specifically to facilitate synergies in research and development in the areas of multimedia forensics, forensicbiometrics and forensic science. IWBF provides the meeting place for those concerned with the usage of multimedia analysis in forensic applications and biometric recognition systems, attracting participants from industry, research, academia and end-users.
Internet address

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  • Cite this

    Jia, N., Li, C. T., Sanchez, V., & Liew, A. W-C. (2017). Fast and robust framework for view-invariant gait recognition. In Proceedings - 2017 5th International Workshop on Biometrics and Forensics (IWBF 2017) (pp. 1-6). [7935092] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IWBF.2017.7935092