Face Alignment Based on 3D Morphable Model
Yufeng Zhao, Fan Shi, Meng Ya Zhao, Chen Jia, Shengyong Chen · 2018
Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression method learns a set of generic rules that are sequentially applied to solve the least squares problem. Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far. For example, the result is sensitive to the initial shape and the storage of the model is too large, and no robustness to self-occlusion. This paper proposes an approach to solve that problem. First, we reconstruct the 3D face from a 2D face image with arbitrary poses and expressions. Second, we project the 3D face shape model to the 2D plane, then we can get a 2D face shape. Third, we use the projected 2D face shape as the initial shape for the cascaded regression instead of the generic mean shape. Comparing to the existing methods the proposed method can increase the accuracy of the face alignment under confirming appropriate performance.