  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.
