Predicting Facial Attractiveness from Colour Cues: A New Analytic Framework

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 2; p. 391
• Main Authors: Lu, Yan, Xiao, Kaida, Pointer, Michael, Lin, Yandan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024; MDPI
• Subjects: Accuracy; Asian People; Beauty; China; Color; Cues; Datasets; Esthetics; Experiments; Face; facial attractiveness; Humans; Judgment; machine learning; multivariate regression; Perceptions; predictive accuracy; Regression analysis; Skin; skin colour; Skin Pigmentation; United Kingdom; Variables; United Kingdom; China; United Kingdom > UK; Japan; predictive accuracy; skin colour; facial attractiveness; machine learning; multivariate regression
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24020391

Various facial colour cues were identified as valid predictors of facial attractiveness, yet the conventional univariate approach has simplified the complex nature of attractiveness judgement for real human faces. Predicting attractiveness from colour cues is difficult due to the high number of candidate variables and their inherent correlations. Using datasets from Chinese subjects, this study proposed a novel analytic framework for modelling attractiveness from various colour characteristics. One hundred images of real human faces were used in experiments and an extensive set of 65 colour features were extracted. Two separate attractiveness evaluation sets of data were collected through psychophysical experiments in the UK and China as training and testing datasets, respectively. Eight multivariate regression strategies were compared for their predictive accuracy and simplicity. The proposed methodology achieved a comprehensive assessment of diverse facial colour features and their role in attractiveness judgements of real faces; improved the predictive accuracy (the best-fit model achieved an out-of-sample accuracy of 0.66 on a 7-point scale) and significantly mitigated the issue of model overfitting; and effectively simplified the model and identified the most important colour features. It can serve as a useful and repeatable analytic tool for future research on facial impression modelling using high-dimensional datasets.