Craniofacial reconstruction as a prediction problem using a latent root regression model
Maxime Berar, Yves Rozenholc (University Paris Descartes)

We present a computer-assisted method for facial reconstruction : the method provides an estimation of the facial outlook associated with unidentified skelettal remains. Current computer-assisted methods using a statistical framework rely on a common set of extracted points on the bone and soft-tissue surfaces. Facial reconstruction consists then in predicting the position of the soft-tissue surface points knowing the positions of the bone surface points. We proposed to use Latent Root Regression for the prediction and compare the results obtained to those given by Principal Components Analysis linear models. In conjunction, we have evaluated the influence of the number skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics linking the anatomical landmarks. It enables us to artificially augment the number of skull points. Facial landmarks are obtain using a mesh-matching algorithm between a common reference mesh and the individual soft-tissue surface meshes. The proposed method is validated in term of accuracy, based on a leave-one-out cross-validation test applied on a homogeneous database. Accuracy measures are obtained by computing the distance between the reconstruction and the ground truth. Finally, these results are discussed in regard to current computer-assisted reconstruction facial techniques.