As they are concerned with the physical characteristics of humans, each of these facial imaging areas also falls in the domain of physical anthropology, although not all of them have been traditionally regarded as such.
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Given the breadth of these facial imaging techniques, it is not surprising that a broad array of subject-matter experts participate in and/or contribute to the formulation and implementation of these methods (including forensic odontologists, forensic artists, police officers, electrical engineers, anatomists, geneticists, medical image specialists, psychologists, computer graphic programmers and software developers). This pertains to two craniofacial identification procedures that use skulls and faces-facial approximation and photographic superimposition-as well as face-only methods for age progression/regression, the construction of facial graphics from eyewitness memory (including composites and artistic sketches), facial depiction, face mapping and newly emerging methods of molecular photofitting. Certain cranial measurements need further examination to determine how best to collect data from CT scans in a way that is most compatible with existing dry skull measurements.įacial imaging is a term used to describe methods that use facial images to assist or facilitate human identification. However, successful classifications of FDB group means into CT groups and vice versa (except for Asians) for both sex and ancestry suggest that cranial measurements taken from clinical CT scans from living individuals are comparable to traditional cranial measurements from individuals in osteological databases. CT group means were generally larger than FDB means. From repeat measurements by one observer on a subset of skulls (n = 14) reflecting 14 different CT protocols, the least precise landmark was euryon (SD ≤ 4.09 mm) and the least precise distances according to the coefficient of reliability (< 0.95) were orbital breadth, nasal height, and frontal and parietal chords. Landmarks were placed on 3D surface models of the skulls to approximate traditional cranial measurements utilized in sex and ancestry estimations. Classification accuracy was estimated by a leave-one-out cross validation, and group means were compared to the Forensic Data Bank (FDB). The accuracy and matching results suggest that facial features are highly influenced by the underlying craniofacial skeleton regardless of weight/age variations, the matching model is predictive of automated facial recognition performance, and that ReFace approximations may be sufficiently accurate to biometrically match unidentified decedents to missing persons.Ĭraniometric data from computed tomography (CT) head scans of 287 living Americans of three descent groups (African, Asian, European) and both sexes were analyzed for measurement precision. A statistical matching model that modeled performance of automated facial recognition correctly identified 73%-88% of known-approximation pairs as matches. The largest errors were associated with cheilion and indicated underestimated mouth widths by an overall average of 1.92 mm (SD = 3.99 mm). For all individual ILD errors combined, 99.5% were within ± 10.0 mm, 90.8% were within ± 5.0 mm, 64.6% were within ± 2.5 mm, and 30.2% were within ± 1.0 mm. Quantitative comparisons of corresponding three-dimensional (3D) known faces and approximations were analyzed using 66 inter-landmark distances (ILDs) derived from twelve interior facial landmarks.Īverage signed errors per ILD ranged from -0.67 mm to 1.92 mm, with most errors indicating smaller ILDs in approximations. This study uses a leave-one-out procedure to generate average approximations (not adjusted for weight/age) for each individual in the ReFace reference database (n = 388), consisting of eight sex/ancestry groups (both sexes for each: African, Asian, European, Hispanic). A geometric morphometric approach for testing the metric accuracy of a computerized facial approximation program is presented.