Craniofacial identification standards: A review of reliability, reproducibility, and implementation.

There are numerous anatomical and anthropometrical standards that can be utilised for craniofacial analysis and identification. These standards originate from a wide variety of sources, such as orthodontic, maxillofacial, surgical, anatomical, anthropological and forensic literature, and numerous media have been employed to collect data from living and deceased subjects. With the development of clinical imaging and the enhanced technology associated with this field, multiple methods of data collection have become accessible, including Computed Tomography, Cone-Beam Computed Tomography, Magnetic Resonance Imaging, Radiographs, Three-dimensional Scanning, Photogrammetry and Ultrasound, alongside the more traditional in vivo methods, such as palpation and direct measurement, and cadaveric human dissection. Practitioners often struggle to identify the most appropriate standards and research results are frequently inconsistent adding to the confusion. This paper aims to clarify how practitioners can choose optimal standards, which standards are the most reliable and when to apply these standards for craniofacial identification. This paper describes the advantages and disadvantages of each mode of data collection and collates published research to review standards across different populations for each facial feature. This paper does not aim to be a practical instruction paper; since this field encompasses a wide range of 2D and 3D approaches (e.g., clay sculpture, sketch, automated, computer-modelling), the implementation of these standards is left to the individual practitioner.

A spatially-dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction.

Forensic Craniofacial Reconstruction (CFR) is an investigative technique used to illicit recognition of a deceased person by reconstructing the most likely face starting from the skull. A key component in most CFR methods are estimates of facial soft tissue depths (TD) at particular points (landmarks) on the skull based on averages from databases of TD recordings. These databases vary in their method of extraction, number and position of landmarks (usually sparse <100), condition of the body, population studied, and sub-categorization of the data. In this work a new dataset is presented in a novel manner based on 156 CT scans using a spatially-dense set (∼7500) of TD recordings to allow for a complete understanding of TD variation interpolating between typical landmarks. Furthermore, to unravel the interplay between soft-tissue layers, skull and facial morphology, TD and Facial Form (FF) are investigated both separately and combined. Using a partial least squares regression (PLSR) analysis, which allows for working with multivariate and spatially-dense data, on metadata of Sex, Age and BMI, different significant patterns on TD and FF variation were found. A similar, but with TD and FF combined, PLSR generated a model useful to report on both, in function of Sex, Age and BMI. In contrast to other datasets and due to the continuous nature of the regression there is no need for data sub-categorization. In further contrast, previous datasets have been presented in tabulated form, which is impractical for spatially-dense data. Instead an interactive tool was built to visualize the regression model in an accessible way for CFR practitioners as well as anatomists. The tool is free to the community and forms a base for data contributions to augment the model and its future use in practice.