Topographic feature mapping for head pose estimation with application to facial gesture interfaces

Authors:
Bisser Raytchev;Ikushi Yoda;Katsuhiko Sakaue
Affiliations:
Intelligent Systems Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan;Intelligent Systems Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan;Intelligent Systems Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
Venue:
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Year:
2005

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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.