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When reconstructing a shooting incident with a shotgun, the muzzle-to-target distance can be determined by relating the size of a dispersion pattern found on a crime scene to that of test shots. Ideally, the test shots are performed with the weapon and ammunition that were used in the incident. But sometimes examiners will have to resort to alternatives, such as using cartridges of the same brand and type but with another pellet size. For this reason, the relationship between pellet size and shotgun dispersion patterns was studied with both lead and steel shotgun pellets. Cartridges were loaded with identical cartridge cases, powder charges, and wads but with different pellet sizes, below size B. The cartridges were fired, and the dispersion patterns at 5 m in front of the muzzle were measured and compared. The results provide strong support for the proposition that shotgun dispersion patterns with both lead and steel shot increase with decreasing pellet size if all other relevant parameters are kept equal. The results also provide an indicative measure of the magnitude of the effect. Pattern sizes were approximately 1.7 times larger with #9 than with #0 lead shot and 1.4 times larger with #9 than with #1 steel shot. The differences between consecutive shot sizes were generally smaller. This means that cartridges of equal brand and type but with the next nearest shot number can be used for a muzzle-to-target distance determination, keeping the information of the current study in mind in the final interpretation of the results., (© 2023 American Academy of Forensic Sciences.)

The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited, despite the relevance for various fields. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching study-wide suggestive association (p < 5 × 10 -8 ), among which 17 were previously unreported. A follow-up multi-ethnic study in additional 7917 individuals confirmed 10 loci including six unreported ones (p adjusted < 2.1 × 10 -3 ). A global map of derived polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis -regulatory activities at the face-associated genetic loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities of several face-associated DNA variants. These results substantially advance our understanding of the genetic basis underlying human facial variation and provide candidates for future in-vivo functional studies., Competing Interests: ZX, GD, LH, ML, PH, Md, GZ, KA, DL, YL, BP, EF, MM, JS, KM, SX, LJ, SW, Fd, BL, SR, AZ, SL, GS, LP, HT, RG, MB, SC, CG, GP, GB, FR, AU, MI, EW, SK, TN, RP, SB, SM, KT, AR, NM, TS, ES, SW, FL, MK No competing interests declared, (© 2019, Xiong et al.)

Measuring facial traits by quantitative means is a prerequisite to investigate epidemiological, clinical, and forensic questions. This measurement process has received intense attention in recent years. We divided this process into the registration of the face, landmarking, morphometric quantification, and dimension reduction. Face registration is the process of standardizing pose and landmarking annotates positions in the face with anatomic description or mathematically defined properties (pseudolandmarks). Morphometric quantification computes pre-specified transformations such as distances. Landmarking: We review face registration methods which are required by some landmarking methods. Although similar, face registration and landmarking are distinct problems. The registration phase can be seen as a pre-processing step and can be combined independently with a landmarking solution. Existing approaches for landmarking differ in their data requirements, modeling approach, and training complexity. In this review, we focus on 3D surface data as captured by commercial surface scanners but also cover methods for 2D facial pictures, when methodology overlaps. We discuss the broad categories of active shape models, template based approaches, recent deep-learning algorithms, and variations thereof such as hybrid algorithms. The type of algorithm chosen depends on the availability of pre-trained models for the data at hand, availability of an appropriate landmark set, accuracy characteristics, and training complexity. Quantification: Landmarking of anatomical landmarks is usually augmented by pseudo-landmarks, i.e., indirectly defined landmarks that densely cover the scan surface. Such a rich data set is not amenable to direct analysis but is reduced in dimensionality for downstream analysis. We review classic dimension reduction techniques used for facial data and face specific measures, such as geometric measurements and manifold learning. Finally, we review symmetry registration and discuss reliability.

Landmarking of CT scans is an important step in the alignment of skulls that is key in surgery planning, pre-/post-surgery comparisons, and morphometric studies. We present a novel method for automatically locating anatomical landmarks on the surface of cone beam CT-based image models of human skulls using 2D Gabor wavelets and ensemble learning. The algorithm is validated via human inter- and intra-rater comparisons on a set of 39 scans and a skull superimposition experiment with an established surgery planning software (Maxilim). Automatic landmarking results in an accuracy of 1-2 mm for a subset of landmarks around the nose area as compared to a gold standard derived from human raters. These landmarks are located in eye sockets and lower jaw, which is competitive with or surpasses inter-rater variability. The well-performing landmark subsets allow for the automation of skull superimposition in clinical applications. Our approach delivers accurate results, has modest training requirements (training set size of 30-40 items) and is generic, so that landmark sets can be easily expanded or modified to accommodate shifting landmark interests, which are important requirements for the landmarking of larger cohorts.

Landmarking of 3D facial surface scans is an important analysis step in medical and biological applications, such as genome-wide association studies (GWAS). Manual landmarking is often employed with considerable cost and rater dependent variability. Landmarking automatically with minimal training is therefore desirable. We apply statistical ensemble methods to improve automated landmarking of 3D facial surface scans. Base landmarking algorithms using features derived from 3D surface scans are combined using either bagging or stacking. A focus is on low training complexity of maximal 40 training samples with template based landmarking algorithms that have proved successful in such applications. Additionally, we use correlations between landmark coordinates by introducing a search strategy guided by principal components (PCs) of training landmarks. We found that bagging has no useful impact, while stacking strongly improves accuracy to an average error of 1.7 mm across all 21 landmarks in this study, a 22% improvement as compared to a previous, comparable algorithm. Heritability estimates in twin pairs also show improvements when using facial distances from landmarks. Ensemble methods allow improvement of automatic, accurate landmarking of 3D facial images with minimal training which is advantageous in large cohort studies for GWAS and when landmarking needs change or data quality varies.

In this paper, we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets. Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data. Extracted features include texture, relief map, and transformations thereof. We extend an established 2D landmarking method for simultaneous evaluation of these data. The method is validated by performing landmarking experiments on two data sets using 21 landmarks and compared with an active shape model implementation. On average, landmarking error for our method was 1.9 mm, whereas the active shape model resulted in an average landmarking error of 2.3 mm. A second study investigating facial shape heritability in related individuals concludes that automatic landmarking is on par with manual landmarking for some landmarks. Our algorithm can be trained in 30 min to automatically landmark 3D facial data sets of any size, and allows for fast and robust landmarking of 3D faces.