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This study presents a method, called random forest based tensor regression, for real‐time head pose estimation using both depth and intensity data. The method builds on random forests and proposes to train and use tensor regressors at each leaf node of the trees of the forest. The tensor regressors are trained using both intensity and depth data and their votes are fused. The proposed method is shown to outperform current state of the art approaches in terms of accuracy when applied to the publicly available Biwi Kinect head pose dataset.

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.