Quantification of skin using smartphone and Skip-GANomaly deep learning model in beauty industry

Rui Matsuo; Makoto Hasegawa

doi:10.1117/12.2624589

30 April 2022 Quantification of skin using smartphone and Skip-GANomaly deep learning model in beauty industry

Rui Matsuo, Makoto Hasegawa

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121771Q (2022) https://doi.org/10.1117/12.2624589
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

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.

Citation Download Citation

Rui Matsuo and Makoto Hasegawa "Quantification of skin using smartphone and Skip-GANomaly deep learning model in beauty industry", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121771Q (30 April 2022); https://doi.org/10.1117/12.2624589

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