3D face reconstruction from skull by regression modeling in shape parameter spaces

Abstract Craniofacial reconstruction is to estimate a person׳s face model from the skull. It can be applied in many fields such as forensic medicine, face animation. In this article, a regression modeling based method for craniofacial reconstruction is proposed, in which a statistical shape model is built for skulls and faces, respectively, and the relationship between them is extracted in the shape parameter spaces through partial least squares regression (PLSR). Craniofacial reconstruction is realized by using the relationship and the face statistical shape model. To better represent craniofacial shape variations and boost the reconstruction, both the skull and face are divided into five corresponding feature regions, and a mapping from each skull region to the corresponding face region is established. For an unknown skull, the five face regions are obtained through the five mappings, and the face is recovered by stitching the five face regions. The attributes such as age and body mass index (BMI) can be added into the mappings to achieve the face reconstruction with different attributes. Compared with other statistical learning based methods in literature, the proposed method more directly and reasonably reflects the relationship that the face shape is determined by the skull and influenced by some attributes. In addition, the proposed method does not need to locate landmarks, whose quantity and accuracy can highly affect the reconstruction. Experimental results validate the proposed method.