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.

翻译：面部分布学(LDL)方法最近为面部面部面部面部面部分布学(LDL)提出了方法建议,但目前LDL方法有两个主要问题。首先,标签分布的预期有偏向,导致偏向面部估计。第二,对所有学习样本应用固定分布参数,严重限制了模型能力。在本文中,我们提议采用以Anisotrocic 球形高斯仪(ASG)为基础的LDL方法进行面部面部分布估计。特别是,我们的方法采用球面高斯分布法,这个方法不断产生不偏向的预期。与此同时,我们引入新的损失功能,使网络能够灵活地了解每个学习样本的分布参数。广泛的实验结果显示,我们的方法为AFLW2000和BIWI数据集建立了新的最新状态记录。