Université de Rouen, Laboratoire LITIS, Avenue de l'Université, 76801 Saint-Etienne-du-Rouvray Cedex, France.
Forensic Sci Int. 2011 Jul 15;210(1-3):228-36. doi: 10.1016/j.forsciint.2011.03.010. Epub 2011 Apr 9.
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.
本文提出了一种计算机辅助的面部重建方法。该方法提供了一种对未知骨骼遗骸相关面部形状的估计。目前基于统计框架的计算机辅助方法依赖于位于骨骼和软组织表面的一组共同提取点。大多数面部重建方法都是在已知骨骼表面点位置的情况下，预测软组织表面点的位置。我们建议使用潜在根回归进行预测。然后将得到的结果与主成分分析线性模型的结果进行比较。在此基础上，我们评估了使用的头骨标志点数的影响。通过位于连接这些解剖学标志点的测地线的点，对解剖学标志点进行迭代完成，从而能够人为地增加头骨点的数量。通过在公共参考网格和个体软组织表面网格之间使用网格匹配算法来获得面部点。该方法基于对同质数据库进行的一次留一交叉验证测试，在准确性方面进行了验证。准确性度量通过计算原始面部表面与其重建之间的距离来获得。最后，根据当前的计算机辅助面部重建技术，讨论了这些结果。
