Feature Extraction and HMM-Based Classification of Gait Video Sequences for the Purpose of Human Identification

  • Henryk Josiński
  • Daniel Kostrzewa
  • Agnieszka Michalczuk
  • Adam Świtoński
  • Konrad Wojciechowski
Part of the Studies in Computational Intelligence book series (SCI, volume 481)

Abstract

The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and non-linear (isometric features mapping; Isomap and locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidden Markov model (HMM) was used for the purpose of data classification. The results of the conducted experiments formed the main subject of analysis of classification accuracy expressed by means of the Correct Classification Rate (CCR).

Keywords

dimensionality reduction gait-based human identification Hidden Markov model manifold learning 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Henryk Josiński
    • 1
  • Daniel Kostrzewa
    • 1
  • Agnieszka Michalczuk
    • 1
  • Adam Świtoński
    • 1
  • Konrad Wojciechowski
    • 1
  1. 1.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland

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