BIROn - Birkbeck Institutional Research Online

    Dual phase learning for large scale video gait recognition

    Shen, J. and Pang, H. and Tao, D. and Li, Xuelong (2010) Dual phase learning for large scale video gait recognition. In: Boll, S. and Tian, Q. and Zhang, L. and Zhang, Z. and Chen, Y.-P.P (eds.) Advances in Multimedia Modeling. Lecture Notes in Computer Science 5916. Berlin, Germany: Springer Verlag, pp. 500-510. ISBN 9783642113017.

    Full text not available from this repository.

    Abstract

    Accurate gait recognition from video is a complex process involving heterogenous features, and is still being developed actively. This article introduces a novel framework, called GC2F, for effective and efficient gait recognition and classification. Adopting a ”refinement-and-classification” principle, the framework comprises two components: 1) a classifier to generate advanced probabilistic features from low level gait parameters; and 2) a hidden classifier layer (based on multilayer perceptron neural network) to model the statistical properties of different subject classes. To validate our framework, we have conducted comprehensive experiments with a large test collection, and observed significant improvements in identification accuracy relative to other state-of-the-art approaches.

    Metadata

    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 20 Jun 2013 13:37
    Last Modified: 09 Aug 2023 12:33
    URI: https://eprints.bbk.ac.uk/id/eprint/7541

    Statistics

    Activity Overview
    6 month trend
    0Downloads
    6 month trend
    183Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item