Multimodal random forest based tensor regression

Abstract

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.