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Topographic Feature Mapping for Head Pose Estimation with Application to Facial Gesture Interfaces

  • Bisser Raytchev
  • Ikushi Yoda
  • Katsuhiko Sakaue
Conference paper
  • 941 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)

Abstract

We propose a new general approach to the problem of head pose estimation, based on semi-supervised low-dimensional topographic feature mapping. We show how several recently proposed nonlinear manifold learning methods can be applied in this general framework, and additionally, we present a new algorithm, IsoScale, which combines the best aspects of some of the other methods. The efficacy of the proposed approach is illustrated both on a view- and illumination-varied face database, and in a real-world human-computer interface application, as head pose based facial-gesture interface for automatic wheelchair navigation.

Keywords

Training Sample Singular Value Decomposition Geodesic Distance Locality Preserve Projection Radial Basis Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bisser Raytchev
    • 1
  • Ikushi Yoda
    • 1
  • Katsuhiko Sakaue
    • 1
  1. 1.Intelligent Systems InstituteNational Institute of Advanced Industrial Science and Technology (AIST)Tsukuba, IbarakiJapan

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