Dynamic random regression forests for real-time head pose estimation

摘要

For real-time evaluation of the position and orientation of the human head using depth image, we propose a novel algorithm, the dynamic random regression forests (DRRF), which enhances the conventional random forests (RF) in four aspects. Firstly, the DRRF employs the boosting strategy for data induction to upgrade the learning quality; secondly, the key parameters are optimized in a dynamic manner in order to train the DRRF classifier efficiently; thirdly, a stem operator is integrated into the conventional tree-shaped classifier to increase the possibility of optimum data split; fourthly, a weighted voting scheme utilizes the learning knowledge to determine the regression result more efficiently and accurately. Comparative experiments verify the advantages of the aforementioned four improvement schemes, and demonstrate the DRRF’s accuracy and robustness against partial occlusion and the variations of head pose, illumination, and facial expression.