Unsupervised inference approach to facial attractiveness

TL;DR

This paper introduces an unsupervised probabilistic modeling approach to understand facial attractiveness preferences, revealing that individual preferences encode significant information about subject characteristics.

Contribution

It presents a novel unsupervised inference method using Maximum Entropy and neural networks to model facial preference variations, capturing inter-subject diversity.

Findings

High accuracy in gender classification from sculpted facial vectors

Unsupervised models effectively capture diversity in attractiveness preferences

Preferences reflect relevant information about subjects' characteristics

Abstract

The perception of facial beauty is a complex phenomenon depending on many, detailed and global facial features influencing each other. In the machine learning community this problem is typically tackled as a problem of supervised inference. However, it has been conjectured that this approach does not capture the complexity of the phenomenon. A recent original experiment (Ib\'a\~nez-Berganza et al., Scientific Reports 9, 8364, 2019) allowed different human subjects to navigate the face-space and ``sculpt'' their preferred modification of a reference facial portrait. Here we present an unsupervised inference study of the set of sculpted facial vectors in that experiment. We first infer minimal, interpretable, and faithful probabilistic models (through Maximum Entropy and artificial neural networks) of the preferred facial variations, that capture the origin of the observed inter-subject diversity in the sculpted faces. The application of such generative models to the supervised classification of the gender of the sculpting subjects, reveals an astonishingly high prediction accuracy. This result suggests that much relevant information regarding the subjects may influence (and be elicited from) her/his facial preference criteria, in agreement with the multiple motive theory of attractiveness proposed in previous works.