• 3D imaging;
  • anthropometric mask;
  • facial asymmetry;
  • robust least-squares


Mild facial asymmetries are common in typical growth patterns. Therefore, detection of disordered facial growth patterns in individuals characterized by asymmetries is preferably accomplished by reference to the typical variation found in the general population rather than to some ideal of perfect symmetry, which rarely exists. This presents a challenge in developing an asymmetry assessment tool that is applicable, without modification, to detect both mild and severe facial asymmetries. In this paper we use concepts from geometric morphometrics to obtain robust and spatially-dense asymmetry assessments using a superimposition protocol for comparison of a face with its mirror image. Spatially-dense localization of asymmetries was achieved using an anthropometric mask consisting of uniformly sampled quasi-landmarks that were automatically indicated on 3D facial images. Robustness, in the sense of an unbiased analysis under increasing asymmetry, was ensured by an adaptive, robust, least-squares superimposition. The degree of overall asymmetry in an individual was scored using a root-mean-squared-error, and the proportion was scored using a novel relative significant asymmetry percentage. This protocol was applied to a database of 3D facial images from 359 young healthy individuals and three individuals with disordered facial growth. Typical asymmetry statistics were derived and were mainly located on, but not limited to, the lower two-thirds of the face in males and females. The asymmetry in males was more extensive and of a greater magnitude than in females. This protocol and proposed scoring of asymmetry with accompanying reference statistics will be useful for the detection and quantification of facial asymmetry in future studies.