Craniofacial reconstruction based on heat flow geodesic grid regression (HF-GGR) model

Abstract

Craniofacial reconstruction is to predict the 3D facial geometry according to the internal relationship between the skull and face, which is widely applied in the field of criminal investigation, archaeology, forensic medicine and so on. In this paper, utilizing the inherent advantage of geodesic to encode craniofacial geometry, we propose a heat flow geodesic grid regression (HF-GGR) model to facilitate craniofacial reconstruction. Our algorithm consists of three steps. In the first step, we extract the nose-tip rooted geodesic distance field and discretize it into a radial grid representation. Then in the second step, we generate geodesic grid of target skull appearance by utilizing the partial least squares regression (PLSR) method. Finally in the third step, we reconstruct the face of target skull according to the geodesic grid and face statistical model. We have conducted experiments on a data set with 213 pairs of craniofacial data. The extensive experimental results show that our algorithm can achieve accurate reconstruction results with faster speed and fewer geodesic grid points than the state-of-the-art method.