Abstract Human gait is a useful biometric feature for human identification because it can be perceived remotely without physical contact. One critical step for human gait recognition is to accurately extract visual features. In this paper, we apply the center-symmetric local ternary pattern for feature extraction to identify the person from the gait images. The classification is performed by using a support vector machine. Experiments on the CASIA gait database (Dataset B) are given to illustrate the feasibility of the proposed approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Human Gait Recognition Using GEI-Based Local Texture Descriptors


    Beteiligte:
    Lai, Chih-Chin (Autor:in) / Pan, Shing-Tai (Autor:in) / Wen, Tsung-Pin (Autor:in) / Lee, Shie-Jue (Autor:in)


    Erscheinungsdatum :

    2018-12-01


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Ear biometric recognition using local texture descriptors

    Benzaoui, A. / Hadid, A. / Boukrouche, A. | British Library Online Contents | 2014


    Recognition of human actions using texture descriptors

    Kellokumpu, V. / Zhao, G. / Pietikäinen, M. | British Library Online Contents | 2011



    Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition

    Lazebnik, S. / Schmid, C. / Ponce, J. et al. | British Library Conference Proceedings | 2003


    Gait Classification Using Wavelet Descriptors in Pedestrian Navigation

    Ma, Y. / Hesch, J.A. / Institute of Navigation | British Library Conference Proceedings | 2011