Higher order support tensor regression for head pose estimation

Guo, Weiwei and Kotsia, Irene and Patras, Ioannis (2011) Higher order support tensor regression for head pose estimation. In: 12th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2011), 13 - 15 April, 2011, Delft, The Netherlands.

Full text is not in this repository.

Abstract

In this paper, we exploit the advantages of tensor representations and propose a Supervised Multilinear Learning Model for regression. The model is based on the Canonical (CAN-DECOMP)/Parallel Factors (PARAFAC) decomposition of tensors of multiple modes and allows the simultaneous projection of an input tensor to more than one discriminative directions along each mode. These projection weights are obtained by optimizing a ϵ-insensitive loss functions which leads to generalized Support Tensor Regression (STR). The methods are validated on the problems of head pose estimation using real data from publicly available databases.

Item Type:Conference or Workshop Item (Paper)
Research Areas:School of Science and Technology > Science & Technology
ID Code:9601
Deposited On:27 Nov 2012 07:10
Last Modified:06 Feb 2013 11:34

Repository staff only: item control page

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year