Benchmarking head pose estimation in-the-wild

Abstract

Head pose estimation systems have quickly evolved from simple classifiers estimating a few yaw angles, to the most recent regression approaches that provide precise 3D face orientations in images acquired “in-the-wild”. Accurate evaluation of these algorithms is an open issue. Although the most recent approaches are tested using a few challenging annotated databases, their published results are not comparable. In this paper we review these works, define a common evaluation methodology, and establish a new state-of-the-art for this problem.