论文标题
深度纠缠的学习头摆姿势和面对面对齐在注意力级联和双重条件融合中
Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion
论文作者
论文摘要
头部姿势估计和面部比对构成了依靠面部分析的许多应用的骨干预处理。尽管两者都是密切相关的任务，但通常会单独解决它们，例如通过从地标地点推论头姿势。在本文中，我们建议将注意力级联的脉级融合在一起，并构成头部姿势。该级联使用几何转移网络来集成异质注释以提高里程碑的定位精度。此外，我们提出了一个双重条件融合方案，以根据当前的头部姿势和地标定位估算选择相关的特征图及其区域。我们从经验上表明，将头部姿势和里程碑定位目标纠缠在我们的体系结构中的好处，并且提出的AC-DC模型提高了多个数据库的最新精度，以实现面部对齐和头部姿势估计任务。
Head pose estimation and face alignment constitute a backbone preprocessing for many applications relying on face analysis. While both are closely related tasks, they are generally addressed separately, e.g. by deducing the head pose from the landmark locations. In this paper, we propose to entwine face alignment and head pose tasks inside an attentional cascade. This cascade uses a geometry transfer network for integrating heterogeneous annotations to enhance landmark localization accuracy. Furthermore, we propose a doubly-conditional fusion scheme to select relevant feature maps, and regions thereof, based on a current head pose and landmark localization estimate. We empirically show the benefit of entwining head pose and landmark localization objectives inside our architecture, and that the proposed AC-DC model enhances the state-of-the-art accuracy on multiple databases for both face alignment and head pose estimation tasks.
