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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Raytchev, B., Yoda, I., Sakaue, K. (2005). Topographic Feature Mapping for Head Pose Estimation with Application to Facial Gesture Interfaces. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_18
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DOI: https://doi.org/10.1007/11573425_18
Publisher Name: Springer, Berlin, Heidelberg
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