2 research outputs found
Unsupervised inference approach to facial attractiveness
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.Comment: main article (10 pages, 4 figures) + supplementary information (22
pages, 10 figures). minor typos corrected. Federico Maggiore added as autho