Many-to-many feature point correspondence establishment for region-based facial expression cloning

Abstract

In this thesis, we propose a method to establish a many-to-many feature point correspondence for region-based facial expression cloning. By exploiting the movement coherency of feature points, we first construct a many-to-many feature point matching cross source and target face models. Then we extract super nodes from the relationship of source and target feature points. Source super nodes that show a strong movement coherency are grouped into concrete regions. The source face region segmentation result is transferred to the target face via the one-to-one super node correspondence. After we obtain corresponding regions on source and target faces, we classify face mesh vertices to different regions for later facial animation cloning. Since our method reveals the natural many-to-many feature point correspondence on source and target faces, each region is adaptively sampled by varying number of feature points. Hence the region segmentation result can preserve more mesh deformation information.