Key Parameters of Face Shape Variation in 3D in a Large Sample*

Abstract

Improvement of methods for evidential facial comparison for the Courts relies on the collection of large databases of facial images that permit the analysis of face shape variation and the development of statistical tools. In this paper, we present a short description and key findings of an anthropometric study of face shape variation in three-dimensions. We used Statistical Shape Analysis to investigate a large database sample (n = 1968), classified by age and gender. We found that size, shape of the bilateral features and midline contributed successively to overall variation. Face size is associated with age. Sexual dimorphism is evident in size and shape, and shows patterns that affect male and female subjects differently. We anticipate this approach will lend itself to the development of methods for analysis of variation within subject groups and the establishment of the relative uniqueness or abundance of facial measurements within them.

Anthropological Facial Approximation in Three Dimensions (AFA3D): Computer‐Assisted Estimation of the Facial Morphology Using Geometric Morphometrics

“…Sex, age, corpulence, and centroid size are not the only parameters influencing the facial appearance; other factors need to be assessed to fully understand the individualization of the human face, factors that cannot be easily controlled for or estimated from skeletal remains (i.e., secular trends, diet, environment, biomechanics, muscular activity of the face, etc.) . Nevertheless, our results indicated significant correlations between bone and soft tissues in terms of shape, which confirmed the first quantified observations made by Simpson and Henneberg .…”

Size and Shape Analysis of Error-Prone Shape Data

“…(≈ 5.216), results in the estimated reliability ratio equal to 0.999, suggesting minimal error contamination in the observed facial images. This is also implied by Evison et al (2010), who stated that the three-dimensional coordinates of landmarks were collected with high precision using a software tool. The estimated scale resulting from matching W 1,w onto Y using naive OPA and the conditional score estimate are 0.988 (0.006) and 0.990 (0.006), respectively, with the corresponding estimated standard errors in parentheses.…”

“…Evison et al (2010) analyzed threedimensional facial images of healthy volunteers, which were collected using a Geometrix FaceVision R FV802 Biometric Camera (ALIVE Tech, Cumming, GA). To demonstrate the conditional score method for three-dimensional shape data in comparison with naive OPA, we implement these two methods using data from one volunteer, whose facial configuration consisting of 61 landmarks was measured twice by each of the two operators in the study.…”

Atypical face shape and genomic structural variants in epilepsy

“…Such injuries include fractures, contusions and burns, which often affect the face. Previous facial morphometric studies have ignored facial injuries or excluded such cases on the basis of patients’ recall of injuries (Hammond et al , 2005; Evison et al , 2010; Kau et al , 2010). Our findings held after blinded exclusion of cases with suspected acquired facial deformity.…”

“…The technique has been used in orthodontics (Lane and Harrell, 2008), forensic science (Evison et al , 2010), dysmorphology (Hammond, 2007) and to study variation across ethnic groups (Kau et al , 2010). Dense surface modelling is a statistical method that can be used to analyse surface images, for example, to discriminate between well-known genomic disorders, including Williams syndrome, Smith–Magenis syndrome, 22q11 deletion syndrome, Noonan syndrome, Fabry disease and Cornelia de Lange syndrome (Hammond et al , 2005; Cox-Brinkman et al , 2007).…”