Chapter

Advances in Biometrics

Volume 5558 of the series Lecture Notes in Computer Science pp 1030-1039

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

  • Changyou ChenAffiliated withShanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University
  • , Junping ZhangAffiliated withShanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University
  • , Rudolf FleischerAffiliated withShanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University

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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