## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Facial Reconstruction
Facial soft tissue depths are estimated based on age, sex, corpulence, and craniometrics, and projected using reference planes to obtain the global facial appearance. Position and shape of the eyes, nose, mouth, and ears are inferred from cranial landmarks through geometric morphometrics. The 100 estimated cutaneous landmarks are then used to warp a generic face to the target facial approximation. A validation by re-sampling on a subsample demonstrated an average accuracy of \(\approx\)4 mm for the overall face. The resulting approximation is an objective probable facial shape, but is also synthetic (i.e., without texture), and therefore needs to be enhanced artistically prior to its use in forensic cases. This facial approximation approach is integrated in the TIVMI software and is available freely for further testing. Figure 8: Forensic facial reconstruction is probably the most subjective, and one of the most controversial, techniques in the Forensic Anthropology (FA) field. In addition to remains involved in criminal investigations, facial reconstructions are created for remains believed to be of historical value and for remains of prehistoric hominids and humans. In particular, this figure displays the facial reconstruction of the Homo Heidelbergensis. Image taken from Wikimedia Commons. [PERSON] and [PERSON] [51] combine classical features as the use of a skulls/faces database, and more original aspects: (1) a shape matching method is used to link the unknown skull to the database templates; and (2) the final face is seen as an elastic 3D mask that is deformed and adapted onto the unknown skull. In this method, the skull is considered as a whole surface and not restricted to some anatomical landmarks, allowing a dense description of the skull/face relationship. [PERSON] and [PERSON] [197] employed a database of portrait photos to create many face candidates, and then performed a superimposition. First, they build an effective autoencoder for image-based facial reconstruction and, second, they use a generative model for constrained face inpainting. Their experiments have demonstrated that the proposed pipeline was stable and accurate. [PERSON] et al. [198] developed a software solution for 3D facial approximation from the skull based on CT scans of the head obtained from 59 Japanese adult volunteers (40 males, 19 females). The positional relationship between the skull and head surface shape were analyzed by creating anatomically homologous shape models. Before modeling, skull shapes were simplified by concealing hollow structures of the skull. Surficial tissue thickness, represented by the distance between corresponding vertices of the simplified skull and head surface, was calculated for each individual, and averaged for each sex. Although the approximate head shapes of known individuals showed a relatively good resemblance in both the shape of the whole head and facial parts, some errors were identified, particularly in areas with thick superficial tissue at the cheek, and thicker tissue at the glabella, nose, mouth, and chin. Moreover, they created referential models for CFS from average models of the skull and head surface shape for each sex. Recently developed computer-enabled tools have facilitated the estimation of face shape from genetic sequences [200,201]. Called \"molecular photofitting\" [202], these methods can supplement facial approximation methods to predict faces being especially useful for morphologies with limited tangible relationships to the skeletal structure. In terms of specific face traits, red hair color and blue/brown colors of the iris are regarded as accurately predictable from genes alone [203]. Approximately 70% accuracy has been recorded for red hair prediction [204] whereas positive predictive intervals of colours of the iris ranged from 66-100% for blue eyes and 70-100% for brown eyes [205, 206, 207, 208, 209].