Appearance manifold of facial expression

  • Authors:
  • Caifeng Shan;Shaogang Gong;Peter W. McOwan

  • Affiliations:
  • Department of Computer Science, Queen Mary, University of London, London, UK;Department of Computer Science, Queen Mary, University of London, London, UK;Department of Computer Science, Queen Mary, University of London, London, UK

  • Venue:
  • ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
  • Year:
  • 2005

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Abstract

This paper investigates the appearance manifold of facial expression: embedding image sequences of facial expression from the high dimensional appearance feature space to a low dimensional manifold. We explore Locality Preserving Projections (LPP) to learn expression manifolds from two kinds of feature space: raw image data and Local Binary Patterns (LBP). For manifolds of different subjects, we propose a novel alignment algorithm to define a global coordinate space, and align them on one generalized manifold. Extensive experiments on 96 subjects from the Cohn-Kanade database illustrate the effectiveness of the alignment algorithm. The proposed generalized appearance manifold provides a unified framework for automatic facial expression analysis.