Head Pose Estimation by Nonlinear Manifold Learning

  • Authors:
  • Bisser Raytchev;Ikushi Yoda;Katsuhiko Sakaue

  • Affiliations:
  • National Institute of Advanced Industrial Science and Technology (AIST), Japan;National Institute of Advanced Industrial Science and Technology (AIST), Japan;National Institute of Advanced Industrial Science and Technology (AIST), Japan

  • Venue:
  • ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
  • Year:
  • 2004

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Abstract

In this paper we propose an Isomap-based nonlinear alternative to the linear subspace method for manifold representation of view-varying faces. Being interested in user-independent head pose estimation, we extend the Isomap model [A global geometric framework for nonlinear dimensionality reduction] to beable to map (high-dimensional) input data points which are not in the training data set into the dimensionality-reduced space found by the model. From this representation, a pose parameter map relating the input face samples to view angles is learnt. The proposed method is evaluated on a large database of multi-view face images in comparison to two other recently proposed subspace methods.