Facial reconstruction for postmortem identification of humans from their skeletal remains is a challenging and fascinating part of forensic art. The former look of a face can be approximated by predicting and modeling the layers of tissue on the skull. This work is as of today carried out solely by physical sculpting with clay, where experienced artists invest up to hundreds of hours to craft a reconstructed face model. Remarkably, one of the most popular tissue reconstruction methods bears many resemblances with surface fitting techniques used in computer graphics, thus suggesting the possibility of a transfer of the manual approach to the computer. In this paper, we present a facial reconstruction approach that fits an anatomy-based virtual head model, incorporating skin and muscles, to a scanned skull using statistical data on skull / tissue relationships. The approach has many advantages over the traditional process: a reconstruction can be completed in about an hour from acquired skull data; also, variations such as a slender or a more obese build of the modeled individual are easily created. Last not least, by matching not only skin geometry but also virtual muscle layers, an animatable head model is generated that can be used to form facial expressions beyond the neutral face typically used in physical reconstructions.

In forensics, the craniofacial reconstruction is employed as an initialization of the identification from skulls. It is a challenging work to develop such a system due to the ambiguity in the relationship between the shape of the skull and the face. In this paper, we present a facial feature estimation method based on the local structural diversity of skulls. A mapping system between the skull structural measurements and the facial feature shapes is established via a RBF regression model. The PCA subspaces are established for the local facial features and the skull structures. Moreover, we investigate the attribute vector of the facial feature polyhedron and the distance graph of the skull structure as the shape descriptors. The experiments demonstrate the feature outlooks can be estimated feasibly and efficiently. 1.

Craniofacial superimposition is a forensic process in which a photograph of a missing person is compared with a skull found to determine its identity. After one century of development, craniofacial superimposition has become an interdisciplinary research field where computer sciences have acquired a key role as a complement of forensic sciences. Moreover, the availability of new digital equipment (such as computers and 3D scanners) has resulted in a significant advance in the applicability of this forensic identification technique. The purpose of this contribution is twofold. On the one hand, we aim to clearly define the different stages involved in the computer-aided craniofacial superimposition process. Besides, we aim to clarify the role played by computers in the methods considered. In order to accomplish these objectives, an up-to-date review of the recent works is presented along with a discussion of advantages and drawbacks of the existing approaches, with an emphasis on the automatic ones. Future case studies will be easily categorized by identifying which stage is tackled and which kind of computer-aided approach is chosen to face the identification problem. Remaining challenges are indicated