Boosted manifold principal angles for image set-based recognition

Tae Kyun Kim*, Oggie Arandelovic, Roberto Cipolla

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2475-2484
Number of pages10
JournalPattern Recognition
Volume40
Issue number9
DOIs
Publication statusPublished - Sept 2007

Keywords

  • Boosting
  • Canonical correlation analysis
  • Face recognition
  • Illumination
  • Image set
  • Invariance
  • Manifolds
  • Nonlinear subspace
  • Pose
  • Principal angle
  • Robustness

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