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Gait Recognition Using a View Transformation Model in the Frequency Domain

  • Yasushi Makihara
  • Ryusuke Sagawa
  • Yasuhiro Mukaigawa
  • Tomio Echigo
  • Yasushi Yagi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

Gait analyses have recently gained attention as methods of identification of individuals at a distance from a camera. However, appearance changes due to view direction changes cause difficulties for gait recognition systems. Here, we propose a method of gait recognition from various view directions using frequency-domain features and a view transformation model. We first construct a spatio-temporal silhouette volume of a walking person and then extract frequency-domain features of the volume by Fourier analysis based on gait periodicity. Next, our view transformation model is obtained with a training set of multiple persons from multiple view directions. In a recognition phase, the model transforms gallery features into the same view direction as that of an input feature, and so the features match each other. Experiments involving gait recognition from 24 view directions demonstrate the effectiveness of the proposed method.

Keywords

Gait Analysis Gesture Recognition View Direction Perspective Projection Gait Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yasushi Makihara
    • 1
  • Ryusuke Sagawa
    • 1
  • Yasuhiro Mukaigawa
    • 1
  • Tomio Echigo
    • 1
  • Yasushi Yagi
    • 1
  1. 1.Department of Intelligent Media, The Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan

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