Gait has been recently proposed as a biometric feature that, with respect to other human characteristics, can be captured at a distance without requiring the collaboration of the observed subject. Therefore, it turns out to be a promising approach for people identification in several scenarios, e.g. access control and forensic applications. In this paper, we propose an auto- matic gait recognition system based on a set of features acquired using the 3D skeletal tracking provided by the popular Kinect sensor. Gait features are defined in terms of distances between selected sets of joints and their vertical and lateral sway with respect to walking direction. Moreover we do not rely on any geometrical assumptions on the position of the sensor. The effectiveness of the defined gait features is shown in the case of person identification based on supervised classification, using the principal component analysis and the support vector machine. A rich set of experiments is provided in two scenar- ios: a controlled identification setup and a classical video-surveillance setting, respectively. Moreover, we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. Our experimental analysis shows that the proposed method is robust to acquisition settings and achieves very competitive identification accuracy with respect to the state of the art.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Robust gait identification using Kinect dynamic skeleton data



    Erscheinungsdatum :

    01.01.2019



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Gait characterization using dynamic skeleton acquisition

    E. Gianaria / N. Balossino / M. Grangetto et al. | BASE | 2013

    Freier Zugriff

    Kinect-Based Gait Analysis for People Recognition Over Time

    GIANARIA, ELENA / GRANGETTO, Marco / BALOSSINO, Nello | BASE | 2017

    Freier Zugriff


    Hierarchical long short-term memory for action recognition based on 3D skeleton joints from Kinect sensor

    Hidayanto, Nur Awal / Prahara, Adhi / Puriyanto, Riky Dwi | BASE | 2021

    Freier Zugriff

    Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson’s Disease Using the Azure Kinect Sensor

    Claudia Ferraris / Gianluca Amprimo / Giulia Masi et al. | BASE | 2022

    Freier Zugriff