Individual Identification Using Gait Sequences under Different Covariate Factors

  • Yogarajah Pratheepan
  • Joan V. Condell
  • Girijesh Prasad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5815)


Recently, gait recognition for individual identification has received increased attention from biometrics researchers as gait can be captured at a distance using low-resolution capturing device. Human gait properties can be affected by different clothing and carrying objects (i.e. covariate factors). Most of the literature shows that these covariate factors give difficulties for individual identification based on gait. In this paper, we propose a novel method that generates dynamic and static feature templates of the sequences of silhouette images (Dynamic Static Silhouette Templates (DSSTs)) to overcome this issue. Here the DSST is calculated from Motion History Images (MHIs). The experimental results show that our method overcomes issues arising from differing clothing and the carrying of objects.


Covariate Factor Foreground Pixel Symmetric Axis Gait Recognition Distance Transform 
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 2009

Authors and Affiliations

  • Yogarajah Pratheepan
    • 1
  • Joan V. Condell
    • 1
  • Girijesh Prasad
    • 1
  1. 1.School of Computing and Intelligent SystemsUniversity of UlsterUK

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