Putative ratios of facial attractiveness in a deep neural network
Putative ratios of facial attractiveness in a deep neural network
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深度神经网络中面部吸引力的假定比率
DOI：
10.1016/j.visres.2020.10.001
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发表时间：
2021
期刊：
影响因子：
1.8
通讯作者：
Iwaki S
中科院分区：
文献类型：
--
作者：
Tong S.;Liang X.;Kumada T.;Iwaki S
经验证据表明，有一个理想的面部特征（理想比例），可以优化一个人的脸的吸引力。这些假定的比例定义了空间关系方面的面部吸引力，并为测量面部吸引力提供了重要的规则。在本文中，我们证明了深度神经网络（DNN）模型可以在没有明确给出吸引力的注释面部特征的情况下，仅基于分类注释从面部图像中学习推定比率。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，使用四个类别特定神经元（CSN）识别图像中人脸的吸引力（女性/男性×高/低吸引力）。在实验2中，通过反转DNN模型（例如，去卷积）生成人脸图像。这些图像描绘了编码在四个类别的面部吸引力的CSN中的直观属性，并揭示了某些与推定比例相关的证据。在实验3中，模拟的心理物理实验上的人脸图像与不同的推定比例揭示的CSNs的活动的变化是非常相似的人类的判断在以前的研究报告。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征面部吸引力的关键特征。这一发现通过基于DNN的视角方法推进了我们对面部吸引力的理解。
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× 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 (eg, 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.