A coarse-to-fine approach to robust 3D facial landmarking via curvature analysis and Active Normal Model

Facial landmarking is a fundamental step in machine-based face analysis. The majority of existing techniques handle such an issue based on 2D images; however, they suffer from illumination and pose variations that largely degrade landmarking performance. The emergence of 3D data provides us with an alternative to overcome these unsolved problems in the 2D domain. This paper proposes a novel approach to 3D facial landmarking, combining both the advantages of feature based methods as well as model based ones in a coarse-to-fine manner. For the coarse stage, three fiducial landmarks (the nose tip and two inner eye corners) are robustly detected through curvature analysis, and these points are further employed to initialize the subsequent model fitting. For the fine stage, a statistical model is constructed based on the normal information including the x, y, and z components of the facial point-cloud rather than the smooth coordinate information, thereby namely Active Normal Model (ANM), to highlight its shape characteristics for final landmark prediction. The proposed approach accurately localizes 83 fiducial points on each 3D face model, greatly surpassing those of feature based ones, while improving the state of the art model based ones in two aspects, i.e. sensitivity to initialization and deficiency in discrimination. Evaluated on the BU-3DFE database, very competitive results are achieved in comparison with those in the literature, clearly demonstrating its effectiveness.