A novel investigation of the effect of iterations in sliding semi-landmarks for 3D human facial images.

ABSTRACT:

Background

Landmark-based approaches of two- or three-dimensional coordinates are the most widely used in geometric morphometrics (GM). As human face hosts the organs that act as the central interface for identification, more landmarks are needed to characterize biological shape variation. Because the use of few anatomical landmarks may not be sufficient for variability of some biological patterns and form, sliding semi-landmarks are required to quantify complex shape.

Results

This study investigates the effect of iterations in sliding semi-landmarks and their results on the predictive ability in GM analyses of soft-tissue in 3D human face. Principal Component Analysis (PCA) is used for feature selection and the gender are predicted using Linear Discriminant Analysis (LDA) t

SUBMITTER: Nazri A 

PROVIDER: S-EPMC7245916 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature