Real-time 3D head pose estimation using both geometry and learning

In this paper we propose a very efficient and low-cost method for real-time 3D object pose estimation, suitable for operation under severe changes in the illumination conditions. The method consists of a geometry-based and learning-based stage: first a geometry-based pose estimation algorithm is used to obtain pose parameter values, which are utilized as supervised information during a following learning-based stage. The efficacy of the proposed approach is illustrated on a real-world application in a head pose based facial-gesture interface for automatic wheelchair navigation.