This work presents a method for estimating human facial attractiveness, based on supervised learning techniques. Numerous facial features that describe facial geometry, color and texture, combined with an average human attractiveness score for each facial image, are used to train various predictors. Facial attractiveness ratings produced by the final predictor are found to be highly correlated with human ratings, markedly improving previous machine learning achievements. Simulated psychophysical experiments with virtually manipulated images reveal preferences in the machine's judgments which are remarkably similar to those of humans. These experiments shed new light on existing theories of facial attractiveness such as the averageness, smoothness and symmetry hypotheses. It is intriguing to find that a machine trained explicitly to capture an operational performance criteria such as attractiveness rating, implicitly captures basic human psychophysical biases characterizing the perception of facial attractiveness in general. Philosophers, artists and scientists have been trying to capture the nature of beauty since the early days of philosophy. Although in modern days a common layman's notion is that judgments of beauty are a matter of subjective opinion, recent findings suggest that people might share a common taste for facial attractiveness and that their preferences may be an innate part of the primary constitution of our nature. Several experiments have shown that 2 to 8 months old infants prefer looking at faces which adults rate as being more attractive [1] . In addition, attractiveness ratings show very high agreement between groups of raters belonging to the same culture and even across cultures [2] . Such findings give rise to the quest for common factors which determine human facial attractiveness. Accordingly, various hypotheses, from cognitive, evolutional and social perspectives, have been put forward to describe the common preferences for facial beauty. Inspired by Sir Francis Galton's photographic method of composing faces [3], Rubenstein, Langlois and Roggman created averaged faces by morphing multiple images together and proposed that averageness is the answer for facial attractiveness [4, 5] . Human judges found these averaged faces to be attractive and rated them with attractiveness ratings higher than the mean rating of the component faces composing them. Grammer and Thornhill have investigated symmetry and averageness of faces and concluded that symmetry was more important than averageness in facial attractiveness [6] . Little and colleagues have agreed that average faces are attractive but claim that faces with certain extreme features, such as extreme sexually dimorphic traits, may be more attractive than average faces [7] . Other
Kagian et al. REF analyzed facial attractiveness based on facial appearance and geometry.
A Humanlike Predictor of Facial Attractiveness
NIPS
Computer Science