论文标题
面部地标检测的深层结构化预测
Deep Structured Prediction for Facial Landmark Detection
论文作者
论文摘要
现有的基于深度学习的面部标志性检测方法已取得了出色的性能。但是，这些方法并未明确嵌入地标点之间的结构依赖性。因此，它们无法保留地标点之间的几何关系，也无法很好地概括到具有挑战性的条件或看不见的数据。本文提出了一种基于将深度卷积网络与条件随机场相结合的深层面部标志性检测方法。我们证明了它比面部标志性检测中现有的最新技术表现出色的性能，尤其是在包括大姿势和遮挡在内的挑战数据集上具有更好的概括能力。
Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies among landmark points. They hence cannot preserve the geometric relationships between landmark points or generalize well to challenging conditions or unseen data. This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field. We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection, especially a better generalization ability on challenging datasets that include large pose and occlusion.
