Exploiting Depth and Intensity Information for Head Pose Estimation with Random Forests and Tensor Models

Abstract

Real-time accurate head pose estimation is required for several applications. Methods based on 2D images might not provide accurate and robust head pose measurements due to large head pose variations and illumination changes. Robust and accurate head pose estimation can be achieved by integrating intensity and depth information. In this paper we introduce a head pose estimation system that employs random forests and tensor regression algorithms. The former allow the modeling of large head pose variations using large sets of training data, while the latter allow the estimation of more accurate head pose parameters. The combination of the above mentioned methods results in more robust and accurate predictions for large head pose variations. We also study the fusion of different sources of information (intensity and depth images) to determine how their combination affects the performance of a head pose estimation system. The efficiency of the proposed framework is tested on the Biwi Kinect Head Pose dataset, where it is shown that the proposed methodology outperforms typical random forests.