Abstract
Conventional cosmetic human studies rely on pre–post mean comparisons, which have limitations in explaining where and how facial skin changes occur. This pilot single-arm study proposed a privacy-preserving three-dimensional (3D) facial mesh mapping framework and demonstrated its application using an illustrative dataset obtained from participants who used a polydeoxyribonucleotide (PDRN)-containing cosmetic. Twenty-two participants underwent facial skin assessments before and after product use. Conventional analysis included pre–post comparisons of elasticity-related parameters. Additionally, 3D facial images obtained via stereophotogrammetry were converted into de-identified mesh surfaces, spatially aligned between time points, and visualized using color-coded heatmaps. For each participant, the left facial panel displayed changes in a skin hydration permittivity index, while the right panel displayed changes in the R2 gross elasticity parameter (Ua/Uf). Overall mean values tended to increase after product use; however, the 3D visualization revealed heterogeneous spatial patterns undetectable via mean values. This method improved spatial matching, enabled intuitive regional comparison, and reduced privacy concerns by removing identifiable facial features. The privacy-preserving 3D facial mesh mapping (P3DMM) framework may serve as a complementary tool for cosmetic human studies, enabling the generation of structured, de-identified spatial datasets for future skin response research.