Boosted manifold principal angles for image set-based recognition

  • Authors:
  • Tae-Kyun Kim;Ognjen Arandjelović;Roberto Cipolla

  • Affiliations:
  • Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK;Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK;Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

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Abstract

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.