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Craniofacial reconstruction recreates a facial outlook from the cranium based on the relationship between the face and the skull to assist identification. But craniofacial structures are very complex, and this relationship is not the same in different craniofacial regions. Several regional methods have recently been proposed, these methods segmented the face and skull into regions, and the relationship of each region is then learned independently, after that, facial regions for a given skull are estimated and finally glued together to generate a face. Most of these regional methods use vertex coordinates to represent the regions, and they define a uniform coordinate system for all of the regions. Consequently, the inconsistence in the positions of regions between different individuals is not eliminated before learning the relationships between the face and skull regions, and this reduces the accuracy of the craniofacial reconstruction. In order to solve this problem, an improved regional method is proposed in this paper involving two types of coordinate adjustments. One is the global coordinate adjustment performed on the skulls and faces with the purpose to eliminate the inconsistence of position and pose of the heads; the other is the local coordinate adjustment performed on the skull and face regions with the purpose to eliminate the inconsistence of position of these regions. After these two coordinate adjustments, partial least squares regression (PLSR) is used to estimate the relationship between the face region and the skull region. In order to obtain a more accurate reconstruction, a new fusion strategy is also proposed in the paper to maintain the reconstructed feature regions when gluing the facial regions together. This is based on the observation that the feature regions usually have less reconstruction errors compared to rest of the face. The results demonstrate that the coordinate adjustments and the new fusion strategy can significantly improve the craniofacial reconstructions., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Craniofacial reconstruction is important in forensic identification. It aims to estimate a facial appearance for human skeletal remains using the relationship between the soft tissue and the underlying bone structure. Various computerized methods have been developed in recent decades. An effective way is to deform a reference skull to the discovered skull, and then apply the same deformation to the skin associated with the reference skull to provide an approximate face for the discovered skull. For this method, the better the two skulls match each other, the more face-like the reconstructed skin surface will be. In this paper, we present a novel skull registration method that can match the two skulls closely, so as to improve the accuracy of the reconstruction. It combines both global and local deformations. A generic thin-plate spline (TPS)-based deformation, which is global, is applied first to roughly align the two skulls based on two groups of manually defined landmarks. Afterwards, the two skulls are largely matched, except some regions, on which some new landmarks are automatically marked. A compact support radial basis functions (CSRBF)-based deformation, which is local, will then be performed on these regions to adjust the initial alignment of the two skulls. Such adjustment can be repeatedly implemented until the two skulls have optimal alignment. In addition, all the skulls and face involved in the registration are represented by their single outer surfaces to facilitate the reconstruction procedure. The experiments demonstrate that our method can create a plausible face even when the reference skull is very different from the discovered skull. As a result, we can make full use of our database to provide multiple estimates for a principle components analysis (PCA) for the final reconstruction., (Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.)

Craniofacial reconstruction (CFR) is used to recreate a likeness of original facial appearance for an unidentified skull; this technique has been applied in both forensics and archeology. Many CFR techniques rely on the average facial soft tissue thickness (FSTT) of anatomical landmarks, related to ethnicity, age, sex, body mass index (BMI), etc. Previous studies typically employed FSTT at sparsely distributed anatomical landmarks, where different landmark definitions may affect the contrasting results. In the present study, a total of 90,198 one-to-one correspondence skull vertices are established on 171 head CT-scans and the FSTT of each corresponding vertex is calculated (hereafter referred to as densely calculated FSTT) for statistical analysis and CFR. Basic descriptive statistics (i.e., mean and standard deviation) for densely calculated FSTT are reported separately according to sex and age. Results show that 76.12% of overall vertices indicate that the FSTT is greater in males than females, with the exception of vertices around the zygoma, zygomatic arch and mid-lateral orbit. These sex-related significant differences are found at 55.12% of all vertices and the statistically age-related significant differences are depicted between the three age groups at a majority of all vertices (73.31% for males and 63.43% for females). Five non-overlapping categories are given and the descriptive statistics (i.e., mean, standard deviation, local standard deviation and percentage) are reported. Multiple appearances are produced using the densely calculated FSTT of various age and sex groups, and a quantitative assessment is provided to examine how relevant the choice of FSTT is to increasing the accuracy of CFR. In conclusion, this study provides a new perspective in understanding the distribution of FSTT and the construction of a new densely calculated FSTT database for craniofacial reconstruction. [ABSTRACT FROM AUTHOR]