Quantitative Facial Asymmetry

Taylor, Helena O.; Morrison, Clinton S.; Linden, Olivia E.; Phillips, Benjamin; Chang, Jonathan; Byrne, Margaret E.; Sullivan, Stephen R.; Forrest, Christopher R.

doi:10.1097/scs.0b013e3182a2e99d

Cited by 69 publications

(30 citation statements)

References 32 publications

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“…[23] Similar to the present findings, some authors claimed that facial asymmetry was equally prevalent among skeletal Class I, II, and II patients;[24] however, other authors reported that asymmetry is most frequently associated with Class III malocclusions,[1625] or less frequently associated with Class II. [4] The trend toward an increased incidence of facial asymmetry in the Class III population was interesting, but this was not statistically significant that could be due to the composition and size of our sample.…”

Section: Discussionsupporting

confidence: 87%

A retrospective analysis of dentofacial deformities and orthognathic surgeries

Eslamipour

Borzabadi‐Farahani

et al. 2017

Ann Maxillofac Surg

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Section: Discussionsupporting

confidence: 87%

A retrospective analysis of dentofacial deformities and orthognathic surgeries

Eslamipour

Borzabadi‐Farahani

et al. 2017

Ann Maxillofac Surg

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“…Furthermore, our study considers an anatomic subdivision of the face in thirds, that follows the territories of distribution of the main trigeminal branches for somatic sensitivity. Nevertheless, in other studies, the subdivision of the face in thirds was performed, arbitrarily, through horizontal planes [19,20].…”

Section: Discussionmentioning

confidence: 99%

Third-Based Facial Similarities and Differences of Monozygotic Twins: A Stereophotogrammetric 3D Assessment

Pucciarelli¹,

Gibelli²,

Angelis³

et al. 2017

Proceedings of 3DBODY.TECH 2017 - 8th International Conference and Exhibition On 3D Body Scanning and Processing Technologies,

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The genetic background of the craniofacial development is a sensitive topic: in this context twin studies have had a relevant role as they allow to understand the effect of genes on the anatomical variability observed in the population. In particular, monozygotic twins (MZT) share the same DNA and allow to evaluate the genetic component of a specific morphology. In order to understand the similarities and differences on the facial morphology of MZT in different portions of the face, a thirdbased, superimposition approach was applied to the 3D facial scans of 10 couples of MZT. The experimental subjects were acquired through a stereophotogrammetric system, after the identification of a set of reference landmarks on their facial surfaces. The landmarks were used to segment facial areas of interest from the 3D reconstructions and to subdivide them into thirds, according to the territories of distribution of trigeminal branches for somatic sensitivity. The left and right upper, middle and lower facial thirds of each MZT couple were pairwise superimposed and the root mean square (RMS) point-to-point distances were automatically calculated. Data were statistically analysed through a two-way ANOVA, setting the level of significance at 5%. Post-hoc tests were performed with the necessary reductions in the degrees of freedom. Results revealed statistically significant differences among thirds (p < 0.05), while no differences were found for facial sides (p > 0.05) or for the third x side interaction (p > 0.05). Post hoc tests showed statistically significant differences between the upper and the lower facial thirds, and the middle and lower facial thirds (p < 0.05), with the middle and lower thirds being the more different between the couples. In conclusion, stereophotogrammetric techniques can be valid instruments to analyse the facial morphology of MZT. The acquisition procedure is easy to perform, fast and free from risk, being suitable to obtain multiple subsequent 3D reconstructions. These reconstructions can be superimposed and locally analysed in order to provide an anatomically-based, detailed description of the most similar and different facial areas, useful for the evaluation of the genetic components of a specific morphology.

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“…Instead of establishing correspondence across scans, correspondence between halves of the face is sought. To this end, one approach is to first mirror the scan with respect to the x-coordinate (first axis) after registering the face and then to find landmarks being close to each other when comparing the original and mirrored scan (Claes et al, 2011; Taylor et al, 2014).…”

Section: Symmetry Registrationmentioning

confidence: 99%

Quantification of Facial Traits

Böhringer

Jong

2019

Front. Genet.

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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.

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Quantitative Facial Asymmetry

Cited by 69 publications

References 32 publications

A retrospective analysis of dentofacial deformities and orthognathic surgeries

A retrospective analysis of dentofacial deformities and orthognathic surgeries

Third-Based Facial Similarities and Differences of Monozygotic Twins: A Stereophotogrammetric 3D Assessment

Quantification of Facial Traits

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