Statistical Facial Image Characterization of Perceived Translucency Based on Principal Component Analysis

Igarashi, Takanori; Moriguchi, Junji; Naoki, Takahiro; Seo, Masataka; Chen, Yen‐Wei

doi:10.5107/sccj.49.95

Cited by 3 publications

(12 citation statements)

References 7 publications

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“…Yet, restricting the analysis to subjects with lighter or darker phototypes might have led to significantly different outcomes due to skin pigmentation's crucial role in the perception of transparency. 1,5,6,10,[24][25][26] A few studies have already analysed skin characteristics to elucidate which influence transparency or would contribute to its objective evaluation. 1,5,6,[24][25][26] Focussing on a few tens of subjects, often 30, collectively revealed that transparency relates to diffuse reflected light.…”

Section: Predictive Model Of Skin Transparency Using Stepwise Regressionmentioning

confidence: 99%

“…1,5,6,10,[24][25][26] A few studies have already analysed skin characteristics to elucidate which influence transparency or would contribute to its objective evaluation. 1,5,6,[24][25][26] Focussing on a few tens of subjects, often 30, collectively revealed that transparency relates to diffuse reflected light.…”

Section: Predictive Model Of Skin Transparency Using Stepwise Regressionmentioning

confidence: 99%

“…1,5,14 Only two studies relied exclusively on digital images, as we did, but then explored a limited set of parameters. [25][26] Analysing hundreds of parameters acquired entirely with a single fully integrated camera device, we could narrow down factors of interest to a small set of parameters. Their combination highly correlates with transparency scoring by experts.…”

Section: Predictive Model Of Skin Transparency Using Stepwise Regressionmentioning

confidence: 99%

“…A single study represents an exception. Igarashi et al focussed on the entire face, arguing that, transparency being a complex parameter, a global analysis could be relevant 25. As for many studies, our multiple correlation approach focussed on the cheek to keep the number of variable low.…”

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confidence: 99%

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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: Predictive Model Of Skin Transparency Using Stepwise Regressionmentioning

confidence: 99%

Section: Predictive Model Of Skin Transparency Using Stepwise Regressionmentioning

confidence: 99%

Section: Predictive Model Of Skin Transparency Using Stepwise Regressionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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

“…The light source is a light-emitting diode (LED) (R-630 nm, G-525 nm, B-472 nm). Since it has been clarified that an increase in the average brightness of the skin indicates an increase in skin transparency, 3,[11][12][13][14][15][16] the image analysis of our method is based on the image luminance. We focused on smartphone cameras in order to develop a skin transparency measurement method that did not require the use of special equipment.…”

mentioning

confidence: 99%

Pilot study of measurement method of skin transparency using smartphone camera

et al. 2019

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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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Statistical Facial Image Characterization of Perceived Translucency Based on Principal Component Analysis

Cited by 3 publications

References 7 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

Pilot study of measurement method of skin transparency using smartphone camera

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

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