Craniofacial reconstruction based on a hierarchical dense deformable model

Abstract

AbstractCraniofacial reconstruction from skull has deeply been investigated by computer scientists in the past two decades because it is important for identification. The dominant methods construct facial surface from the soft tissue thickness measured at a set of skull landmarks. The quantity and position of the landmarks are very vital for craniofacial reconstruction, but there is no standard. In addition, it is difficult to accurately locate the landmarks on dense mesh without manual assistance. In this article, we propose an automatic craniofacial reconstruction method based on a hierarchical dense deformable model. To construct the model, we collect more than 100 head samples by computerized tomography scanner. The samples are represented as dense triangle mesh to model face and skull shape. As the deformable model demands all samples in uniform form, a non-rigid registration algorithm is presented to align the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose, and mouth. For a given skull, the global and local deformable models are matched with it, and the reconstructed facial surface is obtained by fusing the global and local reconstruction results. To validate our method, a face deformable model is constructed and the reconstruction results are evaluated in its coefficient domain. The experimental results indicate that the proposed method has good performance for craniofacial reconstruction.