论文标题
从单个图像中估算头部姿势估算的ollivier-Ricci曲率
Ollivier-Ricci Curvature For Head Pose Estimation From a Single Image
论文作者
论文摘要
对于许多实际应用，例如注意力和人类行为分析，头部姿势估计是至关重要的挑战。本文旨在通过应用网络曲率概念来估算单个图像的头部姿势。在现实世界中，许多复杂的网络都有一组节点，这些节点彼此相互连接，具有重要的功能角色。同样，面部地标的相互作用可以表示为由加权图建模的复杂动态系统。因此，此类系统的功能与基础图的拓扑和几何形状具有本质上的联系。在这项工作中，使用加权图上的ollivier-Ricci曲率（ORC）作为XGBoost回归模型的输入的几何概念，我们表明，ORC的固有几何基础为发现姿势内的基本共同结构提供了一种自然方法。 BIWI，AFLW2000和指向“ 04数据集的实验表明，ORC_XGB方法的性能与最新方法相比，无论是基于里程碑还是仅图像。
Head pose estimation is a crucial challenge for many real-world applications, such as attention and human behavior analysis. This paper aims to estimate head pose from a single image by applying notions of network curvature. In the real world, many complex networks have groups of nodes that are well connected to each other with significant functional roles. Similarly, the interactions of facial landmarks can be represented as complex dynamic systems modeled by weighted graphs. The functionalities of such systems are therefore intrinsically linked to the topology and geometry of the underlying graph. In this work, using the geometric notion of Ollivier-Ricci curvature (ORC) on weighted graphs as input to the XGBoost regression model, we show that the intrinsic geometric basis of ORC offers a natural approach to discovering underlying common structure within a pool of poses. Experiments on the BIWI, AFLW2000 and Pointing'04 datasets show that the ORC_XGB method performs well compared to state-of-the-art methods, both landmark-based and image-only.
