Robust head pose estimation significantly improves the performance of applications related to face analysis in Cyber-Physical Systems (CPS) such as driving assistance and expression recognition. However, there exist two main challenges in this issue, i.e., the large pose variations and the property of inhomogeneous facial feature space. Head pose in large variations makes the distinguished facial features, such as nose or lips, invisible, especially in extreme cases. Additionally, features extracted from a head do not change in a stationary manner with respect to the head pose, which results in an inhomogeneous feature space. To deal with the above problems, we propose an end-to-end framework to estimate the head pose from a single depth image. To be specific, the PointNet network is adopted to automatically select distinguished facial feature points from visible surface of a head and to extract discriminative features. The Deep Regression Forest is utilized to handle the nonstationary property of the facial feature space and to learn the head pose distributions. Experimental results show that our proposed method achieves the state-of-the-art performance on the Biwi Kinect Head Pose Dataset, the Pandora Dataset and the ICT-3DHP Dataset.

中文翻译：

---

从单个深度图像中学习用于头部姿势估计的深度回归森林

稳健的头部姿势估计显着提高了与网络物理系统 (CPS) 中的面部分析相关的应用程序的性能，例如驾驶辅助和表情识别。然而，在这个问题上存在两个主要挑战，即大的姿态变化和不均匀的面部特征空间的性质。大幅度变化的头部姿势使突出的面部特征（例如鼻子或嘴唇）不可见，尤其是在极端情况下。此外，从头部提取的特征不会相对于头部姿势以静止方式变化，这会导致特征空间不均匀。为了解决上述问题，我们提出了一个端到端的框架来从单个深度图像中估计头部姿势。再具体一点，采用PointNet网络从头部可见表面自动选择可区分的面部特征点并提取判别特征。深度回归森林用于处理面部特征空间的非平稳特性并学习头部姿势分布。实验结果表明，我们提出的方法在 Biwi Kinect 头部姿势数据集、Pandora 数据集和 ICT-3DHP 数据集上实现了最先进的性能。