Pilot study of measurement method of skin transparency using smartphone camera

Nakashima, Yuko; Sato, Hiroka; Shioda, Hiroko; Setou, Mitsutoshi

doi:10.1111/srt.12723

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…5,9 Yet, there seem to be differences in the range of colour leading to increased transparency, as pinpointed by Nishimuta et al 10 Several works have attempted to identify skin parameters important for transparency. 1,5,[11][12][13][14] Overall, they show that colour, colour homogeneity, texture and light reflection are important. Nevertheless, in almost all these studies, skin characteristics were acquired using an entire range of different devices and techniques: spectrophotometer, goniometer, Corneometer ® , confocal microscope, skin replicas, etc., and almost exclusively focussed on the cheek.…”

Section: Introductionmentioning

confidence: 96%

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Analysis of correlation and construction of a predictive model of skin transparency using parameters from digital images of the face

Marin¹,

Honda²,

Nakajima³

et al. 2022

Skin Research and Technology

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Background Skin transparency is a cosmetic asset highly considered by Asian women. Resulting from complex light interactions within the skin, but still not fully understood, there is no simple method to measure it objectively. In this study, skin parameters from digital images were analysed to build a model predicting transparency. Materials and methods Initially, 71 Japanese women (between ages 50 and 60 years) were recruited. This group was then extended to 262 women (between ages 21 and 60 years). Pictures of their faces were taken with the Colorface® under diffuse light and different polarisation angles. Experts graded their transparency using pictures. Pictures were also used to compute 958 skin colour and surface parameters from different regions of the face. Results In the initial group of 71 subjects, 109 parameters correlated with transparency. Half of them are from the cheek and relate to colour or colour homogeneity. If the cheek presented the largest proportion of correlated parameters, best correlations were usually found in other facial regions. Multiple regressions from some cheek parameters can predict up to 80% of transparency. Stepwise regression on parameters from 262 subjects led to a six‐parameter model, which is highly correlated (R = 84.1%) with transparency. It combines skin texture, colour, colour homogeneity and gloss parameters. If half of them are from the cheek, the others are from the tear trough, the full face and the cheekbone. Conclusion Using parameters from digital pictures exclusively, we propose a model that accurately reflects transparency. Including parameters previously shown to relate to transparency, this model should be useful for future dermatology and cosmetic research.

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Section: Introductionmentioning

confidence: 96%

“…Several works have attempted to identify skin parameters important for transparency 1,5,11–14 . Overall, they show that colour, colour homogeneity, texture and light reflection are important.…”

Section: Introductionmentioning

confidence: 99%

Analysis of correlation and construction of a predictive model of skin transparency using parameters from digital images of the face

Marin¹,

Honda²,

Nakajima³

et al. 2022

Skin Research and Technology

View full text Add to dashboard Cite

show abstract

“…proposed a method for measuring skin transparency on the back of the hand using images from an iPhone 7. 12 Li et al. developed a technique for measuring skin surface profiles to monitor skin lesions and mole development using images from an iPhone 13 equipped with an LED light dome.…”

Section: Introductionmentioning

confidence: 99%

“…Nakashima et al. proposed a method for measuring skin transparency on the back of the hand using images from an iPhone 7 12 . Li et al.…”

Section: Introductionmentioning

confidence: 99%

“…11 Nakashima et al proposed a method for measuring skin transparency on the back of the hand using images from an iPhone 7. 12 Li et al developed a technique for measuring skin surface profiles to monitor skin lesions and mole development using images from an iPhone 13 equipped with an LED light dome. 13 Hasegawa et al introduced a method for capturing macro skin images using a smartphone with a ring light of LEDs, alongside an image conversion process to transform these into pseudo-UV images using CycleGAN, a deep learning technique.…”

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confidence: 99%

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A smartphone application for personalized facial aesthetic monitoring

Hashimoto,

Kaneda

2024

Skin Research and Technology

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BackgroundMethods available at home for capturing facial images to track changes in skin quality and evaluate skincare treatments are limited. In this study, we developed a smartphone camera application (app) for personalized facial aesthetic monitoring.Materials and MethodsA face alignment indicators (FAIN) system utilizing facial landmark detection, an artificial intelligence technique, to estimate key facial parts, was implemented into the app to maintain a consistent facial appearance during image capture. The FAIN system is composed of a fixed target indicator and an alignment indicator that dynamically changes its shape according to the user's face position, size, and orientation. Users align their faces to match the alignment indicator with the fixed target indicator, and the image is automatically captured when alignment is achieved.ResultsWe investigated the app's effectiveness in ensuring a consistent facial appearance by analyzing both geometric and colorimetric data. Geometric information from captured faces and colorimetric data from stickers applied to the faces were utilized. The coefficients of variation (CVs) for the L*, a*, and b* values of the stickers were higher compared to those measured by a colorimeter, with CVs of 14.9 times, 8.14 times, and 4.41 times for L*, a*, and b*, respectively. To assess the feasibility of the app for facial aesthetic monitoring, we tracked changes in pseudo‐skin color on the cheek of a participant using skin‐colored stickers. As a result, we observed the smallest color difference ∆Eab of 1.901, which can be considered as the experimentally validated detection limit using images acquired by the app.ConclusionWhile the current monitoring method is a relative quantification approach, it contributes to evidence‐based evaluations of skincare treatments.

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Comprehensive model for characterizing skin translucency by expert grading, panel evaluation and image analysis in a Chinese population

Wang²,

Liu³

et al. 2022

Intern J of Cosmetic Sci

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Objectives Translucent skin is an attribute widely appreciated by people in East Asian countries. There have been studies in the literature to describe the phenomenon by means of clinical grading, instrumental measurement and image analysis. However, due to its subjective and complex nature, skin translucency has not been comprehensively and rigorously characterized and modelled, particularly in the Chinese population. This study is to develop a mathematical model that quantitatively describes skin translucency from visual cues objectively measured from the skin. Materials and Methods The study was designed to characterize and model skin translucency by incorporating expert evaluation, panel perception and image analysis of multiple skin visual attributes in one analysis. Faces of 36 Chinese females aged 18–65 years old were evaluated by a dermatologist to obtain clinical translucency scores. Subject pairs were formed with a relatively high and low translucency score in each pair. Their faces were judged in person by 9 panellists in paired‐comparison (2‐AFC) fashion to pick a ‘more translucent skin’ from each subject pair. Front‐view facial images of the subjects were taken, and multiple colour and other visually perceivable skin attributes were measured using image analysis. Bradley‐Terry analysis and multiple regressions were performed to correlate the panel choices of ‘more translucent skin’ with the objectively measured skin parameters. Results Multiple skin colour properties affected the panel choices towards translucent skin. Among them skin tone lightness and skin glossiness had positive effects on skin translucency while the hue, colour unevenness, severity of red and dark spots affected it negatively. Subsurface light reflection and skin visual smoothness had some effect but were not statistically significant. A mathematical model was constructed to predict a person's skin translucency from objectively measured skin attributes. Conclusion The subjective property of skin translucency can be characterized and quantified via a comprehensive modelling process involving clinical grading, panel evaluation, image‐based measurement of skin attributes and statistical analysis. A novel skin parameter, Skin Translucency Index (STI) was established, which provides a way to measure skin translucency, making it possible to assess treatment efficacy before and after product application.

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Pilot study of measurement method of skin transparency using smartphone camera

Cited by 3 publications

References 9 publications

Analysis of correlation and construction of a predictive model of skin transparency using parameters from digital images of the face

Analysis of correlation and construction of a predictive model of skin transparency using parameters from digital images of the face

A smartphone application for personalized facial aesthetic monitoring

Comprehensive model for characterizing skin translucency by expert grading, panel evaluation and image analysis in a Chinese population

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