A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Skin feature extraction and processing model for statistical skin age estimation
Multimedia Tools and Applications
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With the rapid deployment of information technology and the availability of cheap yet high performance image capturing devices, new types of healthcare services such as self-diagnosis and treatment have become possible. Skin is the outer layer of the human body and has long attracted a great deal of attention, since its appearance conveys useful information on the health condition of the subject. In this paper, we propose a skin age estimation scheme based on its wrinkle features such as length, width and depth, which represents the physical condition of skin statistically and quantitatively. We collected wrinkle features and personal data from various subjects, including age and gender, and constructed the ground truth in consultation with dermatologists. For the estimation, we used a non-linear, multi-class SVM (Support Vector Machine). Via extensive experiments on our prototype system, we show that our scheme achieves a reasonable accuracy.