Human Head Pose Estimation using Multi-Appearance Features

Abstract. Non-verbal interaction signals are of great interest in the research field of natural human-robot interaction. These signals are not limited to gestures and emotional expressions since other signals- like the interpersonal distance and orientation- do also have large influence on the communication process. Therefore, this paper presents a marker-less mono-ocular object pose estimation using a model-to-image registration technique. The object model uses different feature types and visibilities which allow the modeling of various objects. Final experiments with different feature types and tracked objects show the flexibility of the system. It turned out that the introduction of feature visibility allows pose estimations when only a subset of the modeled features is visible. This visibility is an extension to similar approaches found in literature.