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

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 viewand illumination-varied face database, and in a real-world human-computer interface application, as head pose based facial-gesture interface for automatic wheelchair navigation.