Fig 1.
Overview of the data analysis from data acquisition to statistical analysis.
Fig 2.
Computer-generated model faces to facilitate intuitive understanding of aging effects by illustrating site-specific accentuated facial topography.
Left, frontal view of rest posture; second from left, lateral view of rest posture; third from left, contour map of rest posture; third from right, frontal view of smile posture; second from right, lateral view from the right; right, contour map of smile posture.
Fig 3.
Significance maps [top (at rest) and second from the bottom (at peak of smiling)] and difference maps [younger minus older; second from the top (at rest) and bottom (at peak of smiling)].
For the significance maps, blue designates P ≤ 0.05; pale pink, P ≤ 0.01; dark pink, P ≤ 0.001; and purple, P ≤ 0.0001. For the difference maps, red indicates that the younger group exhibited greater values than the older group, whereas blue indicates that the older group exhibited greater values than the younger group. Differences are represented in mm.
Fig 4.
Vectors from the average mesh points of the younger group (arrow base) to those of the older group (arrow apex).
Greater magnitudes are indicated by red and smaller magnitudes by blue. (Left, frontal view; right, lateral view).
Fig 5.
A scatter plot matrix of the principal component (PC) scores for rest and smiling postures in the younger and older groups with a histogram in diagonal cells.
PCs 1–4 explains 67.0% of shape variation across samples. The PC1 shows a clear separation between age groups. Yellow denotes facial configurations at rest in younger group; green, those at rest in older group; red, those at the peak of smiling in younger group; blue, those at the peak of smiling in older group. Shape changes associated with PCs 1–4 are shown in the top column.
Fig 6.
Dendrogram produced by applying the single linkage method to the matrix of Mahalanobis distances between subgroup means.
X-axis, subgroups (i.e., older group and younger group / rest and smile postures); Y-axis, Mahalanobis distances between subgroup means followed by a multifactor analysis of variance (MANOVA).
Table 1.
Multifactor analysis of variance (MANOVA) of the surface-based model.
Table 2.
Discriminant coefficient, standardized discriminant coefficient, partial F-value, and P-value for each of the 11 variables selected in the stepwise analysis for the discriminant function to separate faces in the older group and younger group in the resting posture.
For definition of the variables, please see S1 Appendix, S1–S6 Figs.
Table 3.
Discriminant coefficient, standardized discriminant coefficient, partial F-value, and P-value for each of the 6 variables selected in the stepwise analysis for the discriminant function to separate faces in older group and younger group in the smiling posture.
For definition of the variables, please see please see S1 Appendix, S1–S6 Figs.
Table 4.
Discriminant coefficient, standardized discriminant coefficient, partial F-value, and P-value for each of the 15 variables selected in the stepwise analysis for the discriminant function to separate faces during smiling and resting postures in the younger group.
Table 5.
Discriminant coefficient, standardized discriminant coefficient, partial F-value, and P-value for each of the 2 variables selected in the stepwise analysis for the discriminant function to separate faces during smiling and resting in the older group.