Towards Robust and Automated Head Pose Estimation: Elastic Energy Model

Abstract

Pose estimation is an important part in human face recognition because head rotations will significantly affect the recognition accuracy. However, automated and accurate pose determination still remains an unsolved problem in the research community. In this paper, we propose a novel Elastic Energy Model to tackle this problem, which employs statistical energy contributions of a set of feature points on an input face. It can avoid over-trusting selected anchor points, providing a robust solution to the imprecise feature localization problem that is inevitable in practical applications with cluttered backgrounds. As a general configuration, this model can be easily implemented and extended to other non-rigid objects. Its effectiveness and robustness in automated head pose estimation are investigated in our experiments.