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ICB
2009
Springer

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

14 years 1 months ago
Multilinear Tensor-Based Non-parametric Dimension Reduction for Gait Recognition
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.
Changyou Chen, Junping Zhang, Rudolf Fleischer
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICB
Authors Changyou Chen, Junping Zhang, Rudolf Fleischer
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