BIROn - Birkbeck Institutional Research Online

    Learning a deep-feature clustering model for gait-based individual identification

    Taha, K. and Yoo, Paul and Al-Hammadi, Y. and Muhaidat, S. and Yeun, C.Y. (2023) Learning a deep-feature clustering model for gait-based individual identification. Computers and Security , ISSN 0167-4048.

    cose103559.pdf - Published Version of Record
    Available under License Creative Commons Attribution.

    Download (689kB) | Preview


    Gait biometrics which concern with recognizing individuals by the way they walk are of a paramount importance these days. Human gait is a candidate pathway for such identification tasks since other mechanisms can be concealed. Most common methodologies rely on analyzing 2D/3D images captured by surveillance cameras. Thus, the performance of such methods depends heavily on the quality of the images and the appearance variations of individuals. In this study, we describe how gait biometrics could be used in individuals’ identification using a deep feature learning and inertial measurement unit (IMU) technology. We propose a model that recognizes the biological and physical characteristics of individuals, such as gender, age, height, and weight, by examining high-level representations constructed during its learning process. The effectiveness of the proposed model has been demonstrated by a set of experiments with a new gait dataset generated using a shoe-type based on a gait analysis sensor system. The experimental results show that the proposed model can achieve better identification accuracy than existing models, while also demonstrating more stable predictive performance across different classes. This makes the proposed model a promising alternative to current image-based modeling.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Paul Yoo
    Date Deposited: 30 Oct 2023 16:40
    Last Modified: 31 Oct 2023 10:02


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item