Real Time Head Model Creation and Head Pose Estimation on Consumer Depth Cameras

Abstract

Head pose estimation is an important part of the human perception and is therefore also relevant to make interaction with computer systems more natural. However, accurate estimation of the pose in a wide range is a challenging computer vision problem. We present an accurate approach for head pose estimation on consumer depth cameras that works in a wide pose range without prior knowledge about the tracked person and without prior training of a detector. Our algorithm builds and registers a 3D head model with the iterative closest point algorithm. To track the head pose using this head model an initialization with a known pose is necessary. Instead of providing such an initialization manually we determine the initial pose using features of the head and improve this pose over time. An evaluation shows that our algorithm works in real time with limited resources and achieves superior accuracy compared to other state of the art systems. Our main contribution is the combination of features of the head and the head model generation to build a detector that gives accurate results in a wide pose range.