@inproceedings{f7ffbe92736f49ef8dd1b8fc408fe520,
title = "Multilinear tensor-based non-parametric dimension reduction for gait recognition",
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 = "Dimension reduction, Gait recognition, Multi-linear tensor, Small sample size problem, Subspace learning",
author = "Changyou Chen and Junping Zhang and Rudolf Fleischer",
year = "2009",
doi = "10.1007/978-3-642-01793-3\_104",
language = "English",
isbn = "3642017924",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1030--1039",
booktitle = "Advances in Biometrics - Third International Conference, ICB 2009, Proceedings",
address = "Germany",
note = "3rd IAPR/IEEE International Conference on Advances in Biometrics, ICB 2009 ; Conference date: 02-06-2009 Through 05-06-2009",
}