Heritability of facial morphology

Facial recognition methodologies, widely used today in everything from automatic passport controls at airports to unlocking devices on mobile phones, has developed greatly in recent years. The methodologies vary from feature based landmark comparisons in 2D and 3D, utilising Principal Component Analysis (PCA) to surface-based Iterative Closest Point Algorithm (ICP) analysis and a wide variety of techniques in between. The aim of all facial recognition software (FCS) is to find or match a target face with a reference face of a known individual from an existing database. FCS, however, faces many challenges including temporal variations due to development/ageing and variations in facial expression. To determine any quantifiable heritability of facial morphology using this resource, one has to look for faces with enough demonstrable similarities to predict a possible genetic link, instead of the ordinary matching of the same individual’s face in different instances. With the exception of identical twins, this means the introduction of many more variables into the equation of how to relate faces to each other. Variation due to both developmental and degenerative aging becomes a much greater issue than in previous matching situations, especially when comparing parents with children. Additionally, sexual dimorphism is encountered with cross gender relationships, for example, between mothers and sons. Non-inherited variables are also encountered such as BMI, facial disfigurement and the effects of dental work and tooth loss. For this study a Trimmed Iterative Closest Point Algorithm (TrICP) was applied to three-dimensional surfaces scans, created using a white light scanner and Flexscan 3D, of the faces of 41 families consisting of 139 individuals. The TrICP algorithm produced 7176 Mesh-to-mesh Values (MMV) for each of seven sections of the face (Whole face, Eyes, Nose, Mouth, Eyes-Nose, Eyes-Nose-Mouth, and Eyes-Nose- Mouth-Chin). Receiver Operated Characteristic (ROC) analysis was then conducted for each of the seven sections of the face within 11 predetermined categories of relationship, in order to assess the utility of the method for predicting familial relationships (sensitivity/specificity). Additionally, the MMVs of three single features, (eyes, nose and mouth) were combined to form four combination areas which were analysed within the same 11 relationship categories. Overall the relationship between sisters showed the most similarity across all areas of the face with the clear exception of the mouth. Where female to female comparison was conducted the mouth consistently negatively affected the results. The father-daughter relationship showed the least similarity overall and was only significant for three of the 11 portions of the face. In general, the combination of three single features achieved greater accuracy as shown by Areas Under the Curve (AUC) than all other portions of the face and single features were less predictive than the face as a whole

A novel method for pair-matching using three-dimensional digital models of bone:mesh-to-mesh value comparison

The commingling of human remains often hinders forensic/physical anthropologists during the identification process, as there are limited methods to accurately sort these remains. This study investigates a new method for pair-matching, a common individualization technique, which uses digital three-dimensional models of bone: mesh-to-mesh value comparison (MVC). The MVC method digitally compares the entire three-dimensional geometry of two bones at once to produce a single value to indicate their similarity. Two different versions of this method, one manual and the other automated, were created and then tested for how well they accurately pair-matched humeri. Each version was assessed using sensitivity and specificity. The manual mesh-to-mesh value comparison method was 100 % sensitive and 100 % specific. The automated mesh-to-mesh value comparison method was 95 % sensitive and 60 % specific. Our results indicate that the mesh-to-mesh value comparison method overall is a powerful new tool for accurately pair-matching commingled skeletal elements, although the automated version still needs improvement. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00414-016-1334-3) contains supplementary material, which is available to authorized users

Sex and stature estimation on the tibia:a virtual pilot study on a contemporary Hispanic population

Sex and stature estimation represent two pillars in the creation of the biological profile, providing crucial demographic information that forensic anthropologists use for the identification of unknown skeletonized remains. This pilot study evaluates population data proposing a virtual sex and stature estimation method for a Hispanic population using the tibia. Ninety-two CT scans from the New Mexico Decedent Image Database were used to generate 3D models of the left tibia (forty-seven males, forty-five females). Tibial length, proximal and distal breadth were the parameters taken. Intra-observer error was assessed using an intra-class correlation coefficient. Sex differences were explored, and discriminant function and regression analysis used to develop sex and stature estimation formulae, respectively. High repeatability was demonstrated. Sex estimation accuracies ranged between 83.7 per cent and 93.5 per cent, with proximal and distal breadth showing the highest correct classification rates. Stature estimation produced errors between 5.51 cm and 7 cm, with the validation test providing errors falling within the predicted standard error of the estimate reported by the original equations. This study suggests the potential for accurate sex and stature estimation in the Hispanic sample. Although a larger sample is needed to corroborate the preliminary results, the proposed methods might assist in the identification of future forensic cases