Orthodontists in forensic facial approximation (FFA): current inter-disciplinary perspective

Forensic odontology has contributed significantly in forensic investigations and involves various branches of dentistry including orthodontics. The current communication presents evidence-based perspective highlighting synergistic union of different specialties for Forensic Facial Approximation (FFA). It brings forth commonality in principles of anthropology, forensic science, anthropometry, anatomy, paleontology, forensic odontology, with orthodontics, used in FFA. Various attributes and skills of orthodontists’ aid in dental and skull profiling and the corresponding sex, age, and ethnicity-based soft tissue assessments for facial soft tissue thickness (FSTT), may aid a life-like appearance. They can assist hard tissue profiling by their expertise in growth of skeletal and soft tissue, along with the evolutionary trends in occlusion, and diet formulations. Their knowledge in identifying teeth patterns, dental/skeletal jaw relationships, cranial/facial indices, vertical/horizontal facial proportions, can help prepare skull for orientation and reconstruction. The dental, photographic, and radiographic records maintained by orthodontists and general dentists are instrumental in data retrieval, used in various software, clinical, or research areas. These can provide normative values for comparative analysis or facial recreation. The orthodontists can also assist anthropologists and forensic specialists in the virtual reconstructions due to their ease in using latest digital technologies including three-dimensional (3D) facial scan, stereo-photogrammetry, 3D printing, automated soft-tissue landmarks, growth, and age predictions. Thus, the current study established the commonality in concepts of various forensic disciplines with orthodontics, which can strengthen both forensic on-field facial approximations and hard/soft tissue research to further enhance the accuracy of contemporary digital software used in FFA.

Relationships between the hard and soft dimensions of the nose in Pan troglodytes and Homo sapiens reveal the nasal protrusions of Plio-Pleistocene hominids

By identifying similarity in bone and soft tissue covariation patterns in hominids, it is possible to produce facial approximation methods that are compatible with more than one species of primate. In this study, we conducted an interspecific comparison of the nasomaxillary region in chimpanzees and modern humans with the aim of producing a method for predicting the nasal protrusions of ancient Plio-Pleistocene hominids. We addressed this aim by first collecting and performing regression analyses of linear and angular measurements of nasal cavity length and inclination in modern humans ( Homo sapiens; n = 72) and chimpanzees ( Pan troglodytes ; n = 19), and then by performing a set of out-of-group tests. The first test was performed on two subjects that belonged to the same genus as the training sample, i.e., Homo ( n = 1) and Pan ( n = 1), and the second test, which functioned as an interspecies compatibility test, was performed on Pan paniscus ( n = 1), Gorilla gorilla ( n = 3), Pongo pygmaeus ( n = 1), Pongo abelli ( n = 1), Symphalangus syndactylus ( n = 3), and Papio hamadryas ( n = 3). We identified statistically significant correlations in both humans and chimpanzees with slopes that displayed homogeneity of covariation. Joint prediction formulae were found to be compatible with humans and chimpanzees as well as all other African great apes, i.e., bonobos and gorillas. The main conclusion that can be drawn from this study is that regression models for approximating nasal projection are homogenous among humans and African apes and can thus be reasonably extended to ancestors leading to these clades.

A computerized facial approximation method for archaic humans based on dense facial soft tissue thickness depths

Facial approximation (FA) is a common tool used to recreate the possible facial appearance of a deceased person based on the relationship between soft tissue and the skull. Although this technique has been primarily applied to modern humans in the realm of forensic science and archaeology, only a few studies have attempted to produce FAs for archaic humans. This study presented a computerized FA approach for archaic humans based on the assumption that the facial soft tissue thickness depths (FSTDs) of modern living humans are similar to those of archaic humans. Additionally, we employed geometric morphometrics (GM) to examine the geometric morphological variations between the approximated faces and modern human faces. Our method has been applied to the Jinniushan (JNS) 1 archaic human, which is one of the most important fossils of the Middle Pleistocene, dating back to approximately 260,000 BP. The overall shape of the approximated face has a relatively lower forehead and robust eyebrows; a protruding, wider, and elongated middle and upper face; and a broad and short nose. Results also indicate skull morphology and the distribution of FSTDs influence the approximated face. These experiments demonstrate that the proposed method can approximate a plausible and reproducible face of an archaic human.

MACHINE LEARNING FOR APPROXIMATING UNKNOWN FACE

The problem of facial appearance reconstruction (or facial approximation) basing on a skull is very important as for anthropology and archaeology as for forensics. Recent progress in optical 3D measurements allowed to substitute manual facial reconstruction techniques with computer-aided ones based on digital skull 3D models. Growing amount of data and developing methods for data processing provide a background for creating fully automated technique of face approximation.The performed study addressed to a problem of facial approximation based on skull digital 3D model with deep learning techniques. The skull 3D models used for appearance reconstruction are generated by the original photogrammetric system in automated mode. These 3D models are then used as input for the algorithm for face appearance reconstruction. The paper presents a deep learning approach for facial approximation basing on a skull. It exploits the generative adversarial learning for transition data from one modality (skull) to another modality (face) using digital skull 3D models and face 3D models. A special dataset containing skull 3D models and face 3D models has been collected and adapted for convolutional neural network training and testing. Evaluation results on testing part of the dataset demonstrates high potential of the developed approach in facial approximation.