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Feature Level Multiple Model Fusion Using Multilinear Subspace Analysis with Incomplete Training Set and Its Application to Face Image Analysis

Feng, Z-H, Kittler, J, Christmas, WJ and Wu, X (2013) Feature Level Multiple Model Fusion Using Multilinear Subspace Analysis with Incomplete Training Set and Its Application to Face Image Analysis

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Abstract

In practical applications of pattern recognition and computer vision, the performance of many approaches can be improved by using multiple models. In this paper, we develop a common theoretical framework for multiple model fusion at the feature level using multilinear subspace analysis (also known as tensor algebra). One disadvantage of the multilinear approach is that it is hard to obtain enough training observations for tensor decomposition algorithms. To overcome this difficulty, we adopted the M$^2$SA algorithm to reconstruct the missing entries of the incomplete training tensor. Furthermore, we apply the proposed framework to the problem of face image analysis using Active Appearance Model (AAM) to validate its performance. Evaluations of AAM using the proposed framework are conducted on Multi-PIE face database with promising results.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Feng, Z-HUNSPECIFIEDUNSPECIFIED
Kittler, JUNSPECIFIEDUNSPECIFIED
Christmas, WJUNSPECIFIEDUNSPECIFIED
Wu, XUNSPECIFIEDUNSPECIFIED
Date : 2013
Identification Number : 10.1007/978-3-642-38067-9_7
Contributors :
ContributionNameEmailORCID
PublisherSPRINGER-VERLAG BERLIN, UNSPECIFIEDUNSPECIFIED
Additional Information : The original publication is available at http://dx.doi.org./10.1007/978-3-642-38067-9_7
Depositing User : Symplectic Elements
Date Deposited : 29 Oct 2013 14:48
Last Modified : 09 Jun 2014 13:43
URI: http://epubs.surrey.ac.uk/id/eprint/802295

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