Robust head pose estimation by fusing time-of-flight depth and color

Abstract

We present a new solution for real-time head pose estimation. The key to our method is a model-based approach based on the fusion of color and time-of-flight depth data. Our method has several advantages over existing head-pose estimation solutions. It requires no initial setup or knowledge of a pre-built model or training data. The use of additional depth data leads to a robust solution, while maintaining real-time performance. The method outperforms the state-of-the art in several experiments using extreme situations such as sudden changes in lighting, large rotations, and fast motion.