DeepSkin: Mobile-based Skin Quality Assessment with Deep Learning

Abstract

We present a novel skin quality assessment system with the goal of improving skin care. This system leverages digital dermoscopy and consists of hardware, software, and a machine learning component to automate the process of skin quality assessment. We first give a brief introduction to the problem followed by and in-depth overview of skin assessment and related works. Following this, the different components of the system are described along with the data collection and algorithms. We performed two studies with 114 participants in total. These studies yielded 2332 skin images that were ranked by experts across a number of skin quality metrics. We plan to release this dataset as part of this work. Using this data we trained several classical and deep learning algorithms to classify Dryness, Frailty, and existence of hair for the images. The best performance for Dryness detection was achieved by VGG16 with an accuracy of 0.8606, while Frailty was best classified by VGG19 with an accuracy of 0.7929. Additionally, Hair was best detected by VGG16 with an accuracy of 0.8756 We believe our proposed and developed system creates a new research direction in the area of digital mobile-based dermoscopy, showing the feasibility of low-cost, mobile, and reliable skin quality assessment.