Empirical evidence has shown that there is an ideal arrangement of facial features (ideal ratios) that can optimize the attractiveness of a person’s face. These putative ratios define facial attractiveness in terms of spatial relations and provide important rules for measuring the attractiveness of a face. In this paper, we show that a deep neural network (DNN) model can learn putative ratios from face images based only on categorical annotation when no annotated facial features for attractiveness are explicitly given. To this end, we conducted three experiments. In Experiment 1, we trained a DNN model to recognize the attractiveness (female/male $\times$ high/low attractiveness) of face in the images using four category-specific neurons (CSNs). In Experiment 2, face-like images were generated by reversing the DNN model (e.g., deconvolution). These images depict the intuitive attributes encoded in CSNs of the four categories of facial attractiveness and reveal certain consistencies with reported evidence on the putative ratios. In Experiment 3, simulated psychophysical experiments on face images with varying putative ratios reveal changes in the activity of the CSNs that are remarkably similar to those of human judgements reported in a previous study. These results show that the trained DNN model can learn putative ratios as key features for the representation of facial attractiveness. This finding advances our understanding of facial attractiveness via DNN-based perspective approaches.

经验证据表明，存在可以优化人脸吸引力的面部特征的理想排列（理想比例）。这些假定的比率根据空间关系定义了面部吸引力，并提供了衡量面部吸引力的重要规则。在本文中，我们展示了深度神经网络 (DNN) 模型可以仅基于分类注释从面部图像中学习假定的比率，当没有明确给出吸引力的注释面部特征时。为此，我们进行了三个实验。在实验 1 中，我们训练了一个 DNN 模型来识别吸引力（女性/男性 $\times$ 使用四个特定类别神经元 (CSN) 的图像中人脸的高/低吸引力）。在实验 2 中，通过反转 DNN 模型（例如，去卷积）生成类人脸图像。这些图像描绘了在 CSN 中编码的四类面部吸引力的直观属性，并揭示了与假定比率的报告证据的某些一致性。在实验 3 中，对具有不同假定比率的面部图像进行模拟心理物理学实验揭示了 CSN 活动的变化，这与先前研究中报告的人类判断的变化非常相似。这些结果表明，经过训练的 DNN 模型可以学习假定的比率作为表示面部吸引力的关键特征。这一发现促进了我们通过基于 DNN 的透视方法对面部吸引力的理解。