Unsupervised inference approach to facial attractiveness.

ABSTRACT: The perception of facial attractiveness is a complex phenomenon which depends on how the observer perceives not only individual facial features, but also their mutual influence and interplay. In the machine learning community, this problem is typically tackled as a problem of regression of the subject-averaged rating assigned to natural faces. However, it has been conjectured that this approach does not capture the complexity of the phenomenon. It has recently been shown that different human subjects can 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 such experiments. We first infer minimal, interpretable and accurate probabilistic models (through Maxi

SUBMITTER: Ibanez-Berganza M 

PROVIDER: S-EPMC7602690 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature