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

• Published in: Skin research and technology Vol. 28; no. 4; pp. 582 - 595
• Main Authors: Marin, Quentin, Honda, Tatsuya, Nakajima, Hiroaki, Okano, Yuri, Cherel, Marie, Prestat‐Marquis, Elodie
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.07.2022; John Wiley and Sons Inc
• Subjects: Adult; Cheek; Color; Colorface; Dermatology; Digital imaging; Face; Face - diagnostic imaging; Female; Gloss; Homogeneity; Humans; Mathematical models; Middle Aged; Original; Parameters; Pictures; Prediction models; predictive model; Skin; Skin - diagnostic imaging; Skin Aging; Skin Pigmentation; skin transparency; visual clues; Young Adult; visual clues; skin transparency; predictive model; Colorface
• ISSN: 0909-752X; 1600-0846; 1600-0846
• DOI: 10.1111/srt.13161

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.