Craniofacial reconstruction as a prediction problem using a Latent Root Regression model

TL;DR

This paper introduces a novel facial reconstruction method using Latent Root Regression to predict facial shapes from skull data, improving accuracy over traditional PCA-based models.

Contribution

It applies Latent Root Regression to craniofacial reconstruction, compares it with PCA models, and evaluates the impact of landmark quantity on prediction accuracy.

Findings

Latent Root Regression outperforms PCA in facial shape prediction.

Increasing skull landmarks improves reconstruction accuracy.

The method achieves high accuracy in leave-one-out validation.

Abstract

In this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of extracted points located on the bone and soft-tissue surfaces. Most of the facial reconstruction methods then consist of predicting the position of the soft-tissue surface points, when the positions of the bone surface points are known. We propose to use Latent Root Regression for prediction. The results obtained are then compared to those given by Principal Components Analysis linear models. In conjunction, we have evaluated the influence of the number of skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics which link these anatomical landmarks, thus enabling us to artificially increase the number of skull points. Facial points are obtained using a mesh-matching algorithm between a common reference mesh and individual soft-tissue surface meshes. The proposed method is validated in term of accuracy, based on a leave-one-out cross-validation test applied to a homogeneous database. Accuracy measures are obtained by computing the distance between the original face surface and its reconstruction. Finally, these results are discussed referring to current computer-assisted reconstruction facial techniques.