Adaptive Head Pose Estimation with Real-Time Structured Light

Abstract

Head pose estimation (HPE) is a crucial task in pose recognition, but the existing HPE methods suffer from low robustness, low accuracy and inconvenience of contact measurement. In this paper, we build up an infrared structured light system for head pose estimation and propose an adaptive head pose estimation method to improve robustness and accuracy without the need for training. Firstly, we utilize a dynamic structured light system to acquire both the standard model and real-time dynamic data. Then, an iterative registration algorithm is proposed to adaptively segment the facial region which remains stable excluding distractions such as expressions and estimate the head pose. We experimentally evaluate the effectiveness of our method under conventional and large-angular head motion, and different expressions. The results demonstrate our method achieves highly accurate and robust real-time head pose estimation in a contactless manner.