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2007

Boosted manifold principal angles for image set-based recognition

11 years 4 months ago
Boosted manifold principal angles for image set-based recognition
In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature. ᭧ 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Tae-Kyun Kim, Ognjen Arandjelovic, Roberto Cipolla
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PR
Authors Tae-Kyun Kim, Ognjen Arandjelovic, Roberto Cipolla
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