Craniofacial reconstruction based on least square support vector regression

Abstract

Craniofacial reconstruction is to get a visual outlook of an individual from its skull. It is an important technology in both forensic medicine and archeology. This paper proposes a novel craniofacial reconstruction method based on least square support vector regression (LSSVR), which has the flexibility for uncovering nonlinear relationships between variables and is easy to solve. We firstly build statistical shape models for skulls and face skins respectively, and then train the LSSVR model in the shape parameter spaces. Given an unknown skull, we project it to the skull shape parameter space, and use the LSSVR model to reconstruct the corresponding face skin. Cross validation is used for parameter selection in LSSVR. Experiments are given on a data set including 150 training pairs of skull and skin samples and 58 testing ones. Comparisons with ridge regression and partial least square regression show that our method can reconstruct the craniofacial effectively and accurately.