Agreement of Face and Face Patch Classification With Clinical Categorization

HC Versus FASHC Versus FAS + PFAS
  • Each classification rate was estimated as the mean area under ROC curves of 20 cross-validation trials and corresponds to the probability of correctly classifying 2 individuals, 1 taken from each of the 2 groups being compared. Closest mean classification labels members of 2 groups by the name of the group whose mean is most similar. For linear discriminant analysis (LDA), the goal is a linear combination of principal component modes that exhibits the largest difference in the subgroup means relative to the within-group variance. Support vector machines, or large margin classifiers, focus on individual cases in the overlap of the subgroups to be classified that help to define a separating surface with largest margin between the subgroups. In addition to the full face, patches of the face were also considered in isolation: periorbit, perioral, perinasal, and profile. CM, closest mean; SVM, support vector machines.