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.

• Type: Journal
• Year: 2007
• Where: PR
• Authors: Tae-Kyun Kim, Ognjen Arandjelovic, Roberto Cipolla
• Keywords: PR 2007, Principal Angle, Principal Angle Fusion, Vector Subspaces