Mathematical Modeling of Aging Effects in Adulthood on the Basis of Smiling Motions with Skin Mechanical Properties

Mathematical modeling of aging effects in adulthood was performed, using the spatio-temporal characteristics in the motion of the facial landmarks with the skin mechanical properties. The smiling motions (MLCR) and the skin properties (firmness and elasticity) were measured for 56 female adults classified into the two age categories in adulthood (young female adult, n=30, middle-aged female adult, n=26), and 57 feature variables (motion, 55, skin property, 2) were extracted from the measurements per smiling motion. The feature vectors composed of the feature variables and the corresponding age categories were stored as the knowledge datasets in the model. The age category of a subject was predicted by the k-nearest neighbor search with the feature vectors in model knowledge datasets and majority voting function. The prediction model was optimized to provide the successful rate of prediction of 84.8%. Additionally, it was revealed that the spatio-temporal characteristics in the motion well differentiated the age categories in adulthood with the skin properties having the appropriate proportionate significance.