KEYWORDS: Skin, Education and training, Deep learning, Neural networks, Ultraviolet radiation, RGB color model, Visualization, Photography, Data modeling, Ultraviolet photography
The face of a subject who had been using cosmeceuticals for four years and eight months was photographed in ultraviolet light and RGB color. These images, including those taken before the use of the cosmetics, were subjected to deep learning using a neural network model referred to as pix2pix. It was demonstrated that it is possible to predict the effect of the cosmeceutical on the skin and generate images for new subjects using a trained neural network.
Human skin visualization and quantification in the beauty industry using a smartphone based on deep learning was discussed in this study. Skin was photographed using a medical camera that could simultaneously capture RGB and UV images of the same area, and a training dataset was generated using the two types of images; the dataset was then trained via U-NET deep learning. The RGB images of the skin captured using a smartphone camera were converted into pseudo-UV images via well-trained U-NET. Moles and age spots could be effectively visualized using the pseudo-UV image. The pseudo-UV images of young subjects were deep-learned via the skip-GANomaly model to quantify the skin of middle-aged subjects.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.