Putative Ratios of Facial Attractiveness in a Deep Neural Network

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 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 facial attractiveness using four category-specific neurons (female/male $\times$ high/low attractiveness). In Experiment 2, face-like images were generated by reversing the DNN model (e.g., deconvolution). These images depict the intuitive attributes of the four categories of facial attractiveness and reveal certain consistencies with reported evidence on the putative ratios of facial attractiveness. In Experiment 3, simulated psychophysical experiments on facial images with varying ratios of features reveal changes in the activity of the category-specific neurons 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. These findings advance our understanding of facial attractiveness and high-level human perception.