Dental development is one of the most widely utilized and accurate methods available for estimating age in subadult skeletal remains. The timing of tooth growth and development is regulated by genetics and less affected by external factors, allowing reliable estimates of chronological age. Traditional methodology focuses on comparing tooth developmental scores to corresponding age charts. Using the Moorrees, Fanning, and Hunt (MFH) developmental scores, Shackelford and colleagues embed the dental development method in a statistical framework based on transition analysis. They generated numerical parameters underlining each “stage” and age-at-death distribution and applied them to fossil hominins and Neanderthals with limited application to modern humans. We use this same method on a subadult test sample (n = 201), representing modern individuals that may become part of the forensic record. We assess the probability coverage of the Shackelford et al. method derived from MFH standards as it applies to all available dentition. Results indicate promise: the age range at 90% and 95% confidence levels includes the chronological age of almost every individual tested. The maximum likelihood age estimates underestimate age by 0.5–2.5 years for individuals 0–15 years of age and by >2.5 years for individuals 16–18 years of age, as previously shown. In an attempt to refine the method, we adjusted the numerical parameters underlying the stages for developing teeth based on a combined modern reference sample (n = 1,964) and tested these revised parameters using the same test sample. The estimated ages from the modified method differ from the original Shackelford et al. methodology by underestimating age to a lesser degree. The modified method does include mean age-at-attainment values for earlier stages of several teeth, allowing for the calculation of narrower confidence intervals. While this study highlights areas of future research in refining dental developmental aging by transition analysis, it also demonstrates that the Shackelford et al. method is applicable and accurate when aging modern subadults in forensic work.

While discriminant function analysis is an inherently Bayesian method, researchers attempting to estimate ancestry in human skeletal samples often follow discriminant function analysis with the calculation of frequentist-based typicalities for assigning group membership. Such an approach is problematic because it fails to account for admixture and for variation in why individuals may be classified as outliers or nonmembers of particular groups. This article presents an argument and methodology for employing a fully Bayesian approach in discriminant function analysis applied to cases of ancestry estimation. The approach requires adding the calculation, or estimation, of predictive distributions as the final step in ancestry-focused discriminant analyses. The methods for a fully Bayesian multivariate discriminant analysis are illustrated using craniometrics from identified population samples within the Howells published data. The article also presents ways to visualize predictive distributions calculated in more than three dimensions, explains the limitations of typicality measures, and suggests an analytical route for future studies of ancestry and admixture based in discriminant function analysis.

Numerous biological and archaeological studies have demonstrated the legitimacy of remote sensing in anthropology. This article focuses on detecting and documenting terrestrial clandestine graves and surface remains (CGSR) of humans using unmanned aerial vehicles (UAVs), sensors, and automatic processing algorithms. CGSR is a problem of complex decision making under uncertainty that requires the identification and intelligent reasoning about direct evidence of human remains and their environmental fingerprints. As such, it is as much an engineering and geospatial problem as it is an anthropology problem. This article is an effort to survey existing work across disciplines and to provide insights and recommendations to assist future research. To support our claims, preliminary experiments were performed at the Forensic Anthropological Research Facility at Texas State University using UAVs, hyperspectral imaging, thermal imaging, and structure from motion. Prior work, our experience, and preliminary results indicate that both great potential and extreme challenges face remote sensing of CGSR.

Computed tomography (CT) scans provide anthropologists with a resource to generate three-dimensional (3D) digital skeletal material to expand quantification methods and build more standardized reference collections. The ability to visualize and manipulate the bone and skin of the face simultaneously in a 3D digital environment introduces a new way for forensic facial approximation practitioners to access and study the face. Craniofacial relationships can be quantified with landmarks or with surface-processing software that can quantify the geometric properties of the entire 3D facial surface. This article describes tools for the generation of dense facial tissue depth maps (FTDMs) using deidentified head CT scans of modern Americans from the Cancer Imaging Archive public repository and the open-source program Meshlab. CT scans of 43 females and 63 males from the archive were segmented and converted to 3D skull and face models using Mimics and exported as stereolithography files. All subsequent processing steps were performed in Meshlab. Heads were transformed to a common orientation and coordinate system using the coordinates of nasion, left orbitale, and left and right porion. Dense FTDMs were generated on hollowed, cropped face shells using the Hausdorff sampling filter. Two new point clouds consisting of the 3D coordinates for both skull and face were colorized on an RGB (red-green-blue) scale from 0.0 (red) to 40.0-mm (blue) depth values and exported as polygon (PLY) file format models with tissue depth values saved in the “vertex quality” field. FTDMs were also split into 1.0-mm increments to facilitate viewing of common depths across all faces. In total, 112 FTDMs were generated for 106 individuals. Minimum depth values ranged from 1.2 mm to 3.4 mm, indicating a common range of starting depths for most faces regardless of weight, as well as common locations for these values over the nasal bones, lateral orbital margins, and forehead superior to the supraorbital border. Maximum depths were found in the buccal region and neck, excluding the nose. Individuals with multiple scans at visibly different weights presented the greatest differences within larger depth areas such as the cheeks and neck, with little to no difference in the thinnest areas. A few individuals with minimum tissue depths at the lateral orbital margins and thicker tissues over the nasal bones (>3.0 mm) suggested the potential influence of nasal bone morphology on tissue depths. This study produced visual quantitative representations of the face and skull for forensic facial approximation research and practice that can be further analyzed or interacted with using free software. The presented tools can be applied to preexisting CT scans, traditional or cone beam, adult or subadult individuals, with or without landmarks, and regardless of head orientation, for forensic applications as well as for studies of facial variation and facial growth. In contrast with other facial mapping studies, this method produced both skull and face points based on replicable geometric relationships, producing multiple data outputs that are easily readable with software that is openly accessible.