Abstract
Skin pigmentation is associated with skin damages and skin cancers, and ultraviolet (UV) photography is used as a minimally invasive mean for the assessment of pigmentation. Since UV photography equipment is not usually available in general practice, technologies emphasizing pigmentation in color photo images are desired for daily care. We propose a new method using conditional generative adversarial networks, named UV-photo Net, to generate synthetic UV images from color photo images. Evaluations using color and UV photo image pairs taken by a UV photography system demonstrated that pigment spots were well reproduced in synthetic UV images by UV-photo Net, and some of the reproduced pigment spots were difficult to be recognized in color photo images. In the pigment spot detection analysis, the rate of pigment spot areas in cheek regions for synthetic UV images was highly correlated with the rate for UV photo images (Pearson’s correlation coefficient 0.92). We also demonstrated that UV-photo Net was effective for floating up pigment spots for photo images taken by a smartphone camera. UV-photo Net enables an easy assessment of pigmentation from color photo images and will promote self-care of skin damages and early signs of skin cancers for preventive medicine.
Highlights
Skin pigmentation is associated with skin damages and skin cancers, and ultraviolet (UV) photography is used as a minimally invasive mean for the assessment of pigmentation
The deep learning model trained under conditional GAN (CGAN) takes input images that belong to the input domain and generates synthetic images suitable for the target domain based on the input images
The discriminator in UV-photo Net classifies the original UV photo image patches and synthetic UV image patches, and the loss based on the classification results from the discriminator is considered in the training process of the generator
Summary
Skin pigmentation is associated with skin damages and skin cancers, and ultraviolet (UV) photography is used as a minimally invasive mean for the assessment of pigmentation. UV-photo Net enables an easy assessment of pigmentation from color photo images and will promote self-care of skin damages and early signs of skin cancers for preventive medicine. Skin hyperpigmentary disorders such as pigment spots and freckles are caused by melanin increase in epidermis and dermis. Since UV photography equipment is not usually placed in general practice, it is desired to develop technologies that detect pigment areas on conventional color photo images taken under visible light in order to promote the assessment of skin pigmentation and early signs of skin cancers in daily life. We propose a new CGAN-based deep learning method, named UV-photo Net, to generate synthetic UV photo images from face skin photo images taken by conventional digital cameras
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