Multilinear Tensor-Based Non-parametric Dimension Reduction for Gait Recognition

  • Changyou Chen
  • Junping Zhang
  • Rudolf Fleischer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

The small sample size problem and the difficulty in determining the optimal reduced dimension limit the application of subspace learning methods in the gait recognition domain. To address the two issues, we propose a novel algorithm named multi-linear tensor-based learning without tuning parameters (MTP) for gait recognition. In MTP, we first employ a new method for automatic selection of the optimal reduced dimension. Then, to avoid the small sample size problem, we use multi-linear tensor projections in which the dimensions of all the subspaces are automatically tuned. Theoretical analysis of the algorithm shows that MTP converges. Experiments on the USF Human Gait Database show promising results of MTP compared to other gait recognition methods.

Keywords

Subspace learning multi-linear tensor small sample size problem dimension reduction gait recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Changyou Chen
    • 1
  • Junping Zhang
    • 1
  • Rudolf Fleischer
    • 1
  1. 1.Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghaiChina

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