Facial landmark localization plays a critical role in many face analysis tasks. In this paper, we present a coarse-to-fine cascaded convolutional neural network system for robust facial landmark localization of faces in the wild. The system consists of two cascaded convolutional neural network levels. The first level network generates an initial prediction of all facial landmarks. The second level networks are cascaded to implement facial component-wise local refinement of the landmark points. We also present a novel data augmentation method for facial landmark localization networks training. The experiment result shows our method outperforms state-of-the-art methods on 300W  common dataset.
Ruiyi Mao, Qian Lin, Jan P. Allebach, "Robust Convolutional Neural Network Cascade for Facial Landmark Localization Exploiting Training Data Augmentation" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, 2018, pp 374-1 - 374-5, https://doi.org/10.2352/ISSN.2470-1173.2018.10.IMAWM-374