Golden Ratio: The Attributes of Facial Attractiveness Learned By CNN

The recent success of deep learning has promoted the applications in facial attractiveness prediction and enhancement. However, what attributes have been learned to represent facial attractiveness is not well discovered yet. In this work, we find that DNN can learn both local and global shape-cues o...

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Bibliographic Details
Main Authors: Liang, Xuefeng, Tong, Song, Kumada, Takatsune, Iwaki, Sunao
Format: Conference Proceeding
Language:English
Subjects:
Biological neural networks
Deconvolutional Neural Network
Face
Facial Attractiveness
Feature extraction
Golden Ratio
Neurons
Support vector machines
Training
Visualization
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Summary:The recent success of deep learning has promoted the applications in facial attractiveness prediction and enhancement. However, what attributes have been learned to represent facial attractiveness is not well discovered yet. In this work, we find that DNN can learn both local and global shape-cues of face (Golden Ratio) that associate with facial attractiveness. This finding is concluded from a newly trained CNN model and an interpretation of visualizing activation of the category-specific neurons. The CNN model is trained on thousands extremely attractive/unattractive face images, and achieves an accuracy of 98.05%. The deconvolutional neural network generates face-like representations that depict the intuitive attributes of four face attractive categories. The results are consistent with the beauty ratios of facial attractiveness in psychological research.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803166