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Human Gait Identification Using Persistent Homology

  • Javier Lamar-León
  • Edel B. García-Reyes
  • Rocío Gonzalez-Diaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

This paper shows an image/video application using topological invariants for human gait recognition. Using a background subtraction approach, a stack of silhouettes is extracted from a subsequence and glued through their gravity centers, forming a 3D digital image I. From this 3D representation, the border simplicial complex ∂ K(I) is obtained. We order the triangles of ∂ K(I) obtaining a sequence of subcomplexes of ∂ K(I). The corresponding filtration F captures relations among the parts of the human body when walking. Finally, a topological gait signature is extracted from the persistence barcode according to F. In this work we obtain 98.5% correct classification rates on CASIA-B database.

Keywords

Simplicial Complex Homology Class Equal Error Rate Gravity Center Topological Invariant 
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 2012

Authors and Affiliations

  • Javier Lamar-León
    • 1
  • Edel B. García-Reyes
    • 1
  • Rocío Gonzalez-Diaz
    • 2
  1. 1.Patterns Recognition Dept.Advanced Technologies Application CenterCuba
  2. 2.Applied Math Dept., School of Computer EngineeringUniv. of SevilleSpain

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