Gender differences in posed smiles using principal component analysis

Abstract

Objectives: The purpose of this study was to clarify gender differences in posed smiles using principal component analysis (PCA).

Materials and methods: Fourteen adult volunteers, 7 males and 7 females, were enrolled. Using the motion analyzing system we developed, range images and 5 × 5 virtual grids were produced across the whole sequence while the volunteers were asked to smile. Two sets of all intersections of the virtual grids captured while the subject was smiling were regarded as PCA variables. Discriminate analysis was then applied to compare the males and females.

Results: The first and second principal component scores (PCSs) were plotted on the x-axis and y-axis, respectively. The center of gravity of the PCSs is shown by the plus on the x-axis and minus on the y-axis for the males and by the minus on the x-axis and the plus on the y-axis for the females. Discriminate analyses of the PCSs revealed a correct gender classification rate of 74.4% for posed smiles.

Conclusions: While the sample size is too small to extrapolate from these results, we can conclude that PCA can be used to identify gender differences while smiling.

Keywords: Discriminate analysis; Gender difference; Motion analysis; Posed smile; Principal component analysis.