Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet

Skin hydration measurement is very important for many clinical studies. Skin capacitive imaging is a novel technique that can be used for in-vivo skin hydration measurements [1-3]. It is based on permittivity measurement principle, and can generate a skin water content image using a matrix sensor. In this paper, we present our latest study on the skin capacitive imaging analysis using Deep Learning GoogLeNet [4]. The skin capacitive images are divided into three groups according to volunteers, gender (male and female), and skin sites (face, forearm, forehead, neck, palm, and lower leg). GoogLeNet is used for image classifications. The results show that GoogLeNet can effectively differentiate the different skin capacitive images from different categories. We will first present the skin capacitive imaging technology and then present the experimental results. This is a post-peer-review, pre-copyedit version of an article published in Advances in Intelligent Systems and Computing.