HeHOP: Highly Efficient Head Orientation and Position Estimation

Abstract

Continuous head pose estimation is an important visual component for human-computer interaction. However, an accurate and computationally efficient method to estimate the head orientation and position remains a challenging task in computer vision. We propose a Highly efficient Head Orientation and Position estimation (HeHOP) approach based on depth data which uses a stage-by-stage regression framework. At each stage, binary features are obtained from local areas of depth information. A global linear mapping is used to predict the head orientation and position update using the binary features. We evaluate our method on the BIWI dataset containing depth images labeled with head orientation and position. The results show that our approach is robust against occlusions and achieves state-of-the-art performance in terms of accuracy, has a low miss rate, and is several times faster than previous methods.