  Facial pose estimation refers to the task of predicting face orientation from
a single RGB image. It is an important research topic with a wide range of
applications in computer vision. Label distribution learning (LDL) based
methods have been recently proposed for facial pose estimation, which achieve
promising results. However, there are two major issues in existing LDL methods.
First, the expectations of label distributions are biased, leading to a biased
pose estimation. Second, fixed distribution parameters are applied for all
learning samples, severely limiting the model capability. In this paper, we
propose an Anisotropic Spherical Gaussian (ASG)-based LDL approach for facial
pose estimation. In particular, our approach adopts the spherical Gaussian
distribution on a unit sphere which constantly generates unbiased expectation.
Meanwhile, we introduce a new loss function that allows the network to learn
the distribution parameter for each learning sample flexibly. Extensive
experimental results show that our method sets new state-of-the-art records on
AFLW2000 and BIWI datasets.
