Craniofacial Reconstruction Using Gaussian Process Latent Variable Models

Craniofacial reconstruction aims at estimating the facial outlook associated to a skull. It can be applied in victim identification, forensic medicine and archaeology. In this paper, we propose a craniofacial reconstruction method using Gaussian Process Latent Variable Models (GP-LVM). GP-LVM is used to represent the skull and face skin data in a low dimensional latent space respectively. The mapping from the skull to face skin is built in the latent spaces by using least square support vector machine (LSSVM) regression model. Experimental results show that the GP-LVM latent space improves the representation of craniofacial data and boosts the reconstruction results compared with the methods in literature.