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  • Fast and robust framework for view-invariant gait recognition

    Author
    Jia, Ning
    Li, Chang-Tsun
    Sanchez, Victor
    Liew, Alan Wee-Chung
    Year published
    2017
    Metadata
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    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 ...
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    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%.
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    Conference Title
    2017 5th International Workshop on Biometrics and Forensics (IWBF 2017)
    DOI
    https://doi.org/10.1109/IWBF.2017.7935092
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/372664
    Collection
    • Conference outputs

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