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Abstract：
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.
摘要：
各种面部颜色线索被确定为面部吸引力的有效预测因子，然而，传统的单变量方法简化了真实人脸吸引力判断的复杂性。由于大量的候选变量及其固有的相关性，很难从颜色线索中预测吸引力。使用来自中国受试者的数据集，这项研究提出了一个新的分析框架，用于从各种颜色特征建模吸引力。实验中使用了一百张真实人脸图像，并提取了65个颜色特征。通过英国和中国的心理物理实验收集了两组独立的吸引力评估数据，作为培训和测试数据集，分别。比较了8种多元回归策略的预测准确性和简单性。所提出的方法实现了对不同面部颜色特征及其在真实面部吸引力判断中的作用的全面评估；提高了预测准确性（最佳拟合模型在7分制上实现了0.66的样本外准确性），并显着减轻了模型过拟合的问题；有效地简化了模型并确定了最重要的颜色特征。它可以作为使用高维数据集进行面部印象建模的未来研究的有用且可重复的分析工具。
