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Human gait recognition via deterministic learning.

Wei Zeng1, Cong Wang

  • 1College of Automation Science and Engineering, South China University of Technology, Guangzhou, China. zw0597@126.com

Neural Networks : the Official Journal of the International Neural Network Society
|September 18, 2012
PubMed
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This study introduces a new model-based approach for human gait recognition using deterministic learning. The method effectively identifies individuals by analyzing gait dynamics, achieving high accuracy on public databases.

Area of Science:

  • Biometrics
  • Pattern Recognition
  • Machine Learning

Background:

  • Human gait recognition is challenging due to its temporal/dynamical nature.
  • Existing methods struggle with time-varying gait signatures.
  • Deterministic learning offers a novel approach for time-invariant representation of dynamical patterns.

Purpose of the Study:

  • To present a new model-based approach for human gait recognition.
  • To leverage deterministic learning for efficient and accurate individual identification.
  • To reduce feature dimensionality while preserving gait dynamics.

Main Methods:

  • Utilized a five-link biped model to extract gait features (joint angles, angular velocities).
  • Employed radial basis function (RBF) neural networks for local identification of gait system dynamics.

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  • Developed a gait signature from phase portrait analysis and stored it in constant RBF networks.
  • Implemented a recognition phase comparing test gait patterns against trained estimators using L(1) norm error.

    • Main Results:

    • Demonstrated effective representation of gait dynamics in a time-invariant manner.
    • Achieved significant feature dimension reduction by focusing on one side of the body.
    • Experimental validation on NLPR and UCSD gait databases confirmed the approach's effectiveness.
    • The method enables rapid recognition based on the smallest error principle.

    Conclusions:

    • The proposed model-based approach effectively recognizes individuals by gait.
    • Deterministic learning and RBF networks provide a robust framework for dynamical pattern recognition.
    • The method offers a promising solution for biometric identification systems.