Classification-Based Skin Aging Analysis Scheme

With the wide-spread interest in various healthcare services, there has been increasing demand for quantitative and objective criteria on which health condition of subjects can be effectively evaluated. Human organs show different symptoms depending on the type of health problem. Among human organs, skin has drawn much attention since it is the outermost part and hence easy to investigate. Skin condition is known to be analyzed and estimated using various features such as wrinkle and elasticity. In this paper, we propose an automatic skin aging evaluation scheme. More specifically, we first collect wrinkle-related features from subjects of different ages and dermatologists evaluate their aging level for the ground truth. And then, we train and compare non-linear, multi-class SVM (Support Vector Machine) using these datasets and ground truth for classification. To evaluate the effectiveness of our proposed scheme, we performed experiments using the SVM. We report some of the result.