Fast Learning for Customizable Head Pose Recognition in Robotic Wheelchair Control

Abstract

In the PLAYBOT project, we aim at assisting disabled children at play. To this end, we are developing a semi autonomous robotic wheelchair. It is equipped with several visual sensors and a robotic manipulator and thus conveniently enhances the innate capabilities of a disabled child. In addition to a touch screen, the child may control the wheelchair using simple head movements. As control based on head posture requires reliable face detection and head pose recognition, we are in need of a robust technique that may effortlessly be tailored to individual users. In this paper, we present a multilinear classification algorithm for fast and reliable face detection. It trains within seconds and thus can easily be customized to the home environment of a disabled child. Subsequent head pose recognition is done using support vector machines. Experimental results show that this two stage approach to head pose-based robotic wheelchair control performs fast and very robust.