Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks

Kojima, Kazuyuki; Shido, Kosuke; Tamiya, Gen; Yamasaki, Kenshi; Kinoshita, Kengo; Aiba, Setsuya

doi:10.1038/s41598-020-79995-4

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…In addition, there exists a causal relationship between skin temperature in the forehead, cheeks, and lips as well as the nasal region and physiological and psychological states, which has been established in our previous study [11]. Studies dealing with facial color have included the assessment of emotion [12]- [14], melanin or hemoglobin distribution [15]- [17], skin pigmentation [18], and the relationship between facial expression and facial color [19]. However, most research on physiological and psychological state assessment based on facial color has been conducted to assess short-term emotions.…”

Section: Introductionmentioning

confidence: 88%

Relationship Between Long-Term Variability of Facial Hue Information in Physiological and Psychological ROIs and Health Condition

Oiwa¹,

Urakami

Lamsal

et al. 2021

IEEE Access

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In this study, we evaluate the relationship between long-term variability of facial hue information in physiological and psychological regions of interests (ROIs) and health condition. Long-term facial hue information, RGB, and L*a*b* color space, were obtained from 13 physiological and psychological ROIs in visible facial images measured for approximately six months. In this study, the evaluation was performed based on four conditions (absolute value of RGB, absolute value of L*a*b*, relative value of RGB, and relative value of L*a*b*). These four long-term variabilities of facial hue information were applied to principal component analysis to clarify the relationship between facial hue information and health condition. The results suggested that long-term variability of chromaticity information around the periorbital region may be related to health conditions. Furthermore, physiological and psychological states related to health condition were estimated with the accuracy of approximately 80% using relative chromaticity information around the periorbital region.

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Section: Introductionmentioning

confidence: 88%

Relationship Between Long-Term Variability of Facial Hue Information in Physiological and Psychological ROIs and Health Condition

Oiwa¹,

Urakami

Lamsal

et al. 2021

IEEE Access

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“…By the way, a method called UV-photo Net, which is closely related to our study, has been proposed [13]. The differences from this method are as follows.…”

Section: Introductionmentioning

confidence: 99%

Skin Visualization Using Smartphone and Deep Learning in the Beauty Industry

Hasegawa

Matsuo

2023

IEICE Trans. Inf. & Syst.

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Human skin visualization in the beauty industry with a smart-phone based on deep learning was discussed. Skin was photographed with a medical camera that could simultaneously capture RGB and UV images of the same area. Smartphone RGB images were converted into versions similar to medical RGB and UV images via a deep learning method called cycle-GAN, which was trained with the medical and the smartphone images. After converting the smartphone image into a version similar to a medical RGB image using cycle-GAN, the processed image was also converted into a pseudo-UV image via a deep learning method called U-NET. Hidden age spots were effectively visualized by this image. RGB and UV images similar to medical images can be captured with a smartphone. Provided the neural network on deep learning is trained, a medical camera is not required.

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“…Various methods for skin image processing using deep learning have been proposed. Most of them generate ultraviolet images from RGB color images of the skin [1,2]. Analysis of the current skin condition is an important issue, and it is an appropriate subject for consideration at the beginning of any study.…”

Section: Introductionmentioning

confidence: 99%

Proposal for predicting the facial brightening effect of cosmeceuticals using deep learning

Matsuo

Yotsumoto²,

Kawanobe³

et al. 2023

International Workshop on Advanced Imaging Technology (IWAIT) 2023

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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.

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Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks

Cited by 13 publications

References 28 publications

Relationship Between Long-Term Variability of Facial Hue Information in Physiological and Psychological ROIs and Health Condition

Relationship Between Long-Term Variability of Facial Hue Information in Physiological and Psychological ROIs and Health Condition

Skin Visualization Using Smartphone and Deep Learning in the Beauty Industry

Proposal for predicting the facial brightening effect of cosmeceuticals using deep learning

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