Abstract
Abstract. Facial appearance has long been understood to offer insight into a person’s health. To an experienced clinician, atypical facial features may signify the presence of an underlying rare or genetic disease. Clinicians use their knowledge of how disease affects facial appearance along with the patient’s physiological and behavioural traits, and their medical history, to determine a diagnosis. Specialist expertise and experience is needed to make a dysmorphological facial analysis. Key to this is accurately assessing how a face is significantly different in shape and/or growth compared to expected norms. Modern photogrammetric systems can acquire detailed 3D images of the face which can be used to conduct a facial analysis in software with greater precision than can be obtained in person. Measurements from 3D facial images are already used as an alternative to direct measurement using instruments such as tape measures, rulers, or callipers. However, the ability to take accurate measurements – whether virtual or not – presupposes the assessor’s facility to accurately place the endpoints of the measuring tool at the positions of standardised anatomical facial landmarks. In this paper, we formally introduce Cliniface – a free and open source application that uses a recently published highly precise method of detecting facial landmarks from 3D facial images by non-rigidly transforming an anthropometric mask (AM) to the target face. Inter-landmark measurements are then used to automatically identify facial traits that may be of clinical significance. Herein, we show how non-experts with minimal guidance can use Cliniface to extract facial anthropometrics from a 3D facial image at a level of accuracy comparable to an expert. We further show that Cliniface itself is able to extract the same measurements at a similar level of accuracy – completely automatically.
Highlights
The human phenotype – including our apparent physiological form – is modified by our genotype and the expression of our genes which is affected by developmental and environmental factors
The violin plots in figure 4 show the distributions of measurement differences over all participants due to the use of different measuring devices by the expert assessor (EX) shown in red, or due to the use of differently generated 3D images by the nonexpert assessors (P1–P4) shown in blue, and by Cliniface (CF) shown in green
The accuracy of measures placed on 3D facial images by both non-expert human assessors and an automated algorithm were evaluated against a baseline set of expert measurements
Summary
The human phenotype – including our apparent physiological form – is modified by our genotype and the expression of our genes which is affected by developmental and environmental factors. A new method was developed using the approach of mapping a 3D anthropometric mask (AM) (Claes et al, 2012) through non-rigid deformation to a target face (White et al, 2019) This method uses an affinity matrix of symmetrically weighted nearest neighbour correspondences between the AM and the target surface. The algorithm’s parameters can be changed to weight the affinity matrix to include or exclude certain parts of the mask or target surface, to change the number of points used in K-Nearest Neighbour correspondence finding, and to change the size of the local smoothing region after each iteration These changes result in greater mapping detail or improved anthropometric correspondence (accuracy of these concerns is traded against one another)
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