Deep 3D Facial Landmark Detection on Position Maps

摘要

3D facial landmark detection is a crucial step for many computer vision applications, such as 3D facial expression analysis, 3D face recognition, and 3D reconstruction. Pose variations, expression changes and self-occlusion yet make 3D facial landmark detection a very challenging task. In this paper, we propose a novel 3D Face Landmark Localization Network (3DLLN), which is robust to the above challenges. Different from existing methods, the proposed 3DLLN utilizes the position maps as an intermediate representation, from which 3DLLN detects 3D landmark coordinates. Further, we demonstrate the usage of a deep regression architecture to improve the accuracy and robustness of a large number of landmarks. The proposed scheme is evaluated on two public datasets FRGCv2 and BU_3DFE and achieves superior results to state-of-the-arts.