Fully automated landmarking and facial segmentation on 3D photographs. B Berends, F Bielevelt, R Schreurs, et al.

A deep learning-based approach for automated landmark extraction from 3dMD facial photographs was developed and its precision was evaluated. The results showed high precision and consistency in landmark annotation, comparable to manual and semi-automatic annotation methods.

Initial Steps towards a Multilevel Functional Principal Components Analysis Model of Dynamical Shape Changes. DJJ Farnell, P Claes.

Multilevel PCA (mPCA) has been used by us to analyze 3D facial shapes obtained from 3D facial scans; note that two-level multilevel PCA (mPCA) is equivalent to bgPCA. mPCA has been used previously to investigate changes by ethnicity and sex, facial shape changes in adolescents due to age, and the effects of maternal smoking and alcohol consumption on the facial shape of English adolescents.