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Automatic gait recognition based on statistical shape analysis.

Liang Wang1, Tieniu Tan, Weiming Hu

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. lwang@nlpr.ia.ac.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study introduces an efficient gait recognition algorithm using statistical shape analysis for automated person identification. The method extracts walking silhouettes and uses shape analysis to create gait signatures for accurate recognition.

Area of Science:

  • Computer Vision
  • Biometrics
  • Pattern Recognition

Background:

  • Automated person identification systems are crucial for visual surveillance and monitoring.
  • Gait recognition offers a non-intrusive method for identifying individuals at a distance.
  • Existing methods often focus on gait dynamics, necessitating new approaches for structural analysis.

Purpose of the Study:

  • To propose a simple and efficient automatic gait recognition algorithm.
  • To utilize statistical shape analysis for gait signature generation.
  • To enable person identification based on the structural characteristics of walking patterns.

Main Methods:

  • Improved background subtraction to extract moving silhouettes from image sequences.
  • Procrustes shape analysis to represent temporal silhouette changes as gait signatures.

Related Experiment Videos

  • Supervised pattern classification using Procrustes distance for recognition.

    • Main Results:

    • The algorithm effectively captures structural gait characteristics, focusing on shape cues.
    • Tested on an outdoor database with 240 sequences from 20 subjects.
    • Demonstrated encouraging performance in gait recognition across different viewing angles.

    Conclusions:

    • The proposed statistical shape analysis method provides an effective approach to gait recognition.
    • Implicitly uses walking action to capture body biometric shape cues for identification.
    • Offers a promising solution for automated person identification in surveillance applications.