  Describable visual facial attributes are now commonplace in human biometrics
and affective computing, with existing algorithms even reaching a sufficient
point of maturity for placement into commercial products. These algorithms
model objective facets of facial appearance, such as hair and eye color,
expression, and aspects of the geometry of the face. A natural extension, which
has not been studied to any great extent thus far, is the ability to model
subjective attributes that are assigned to a face based purely on visual
judgements. For instance, with just a glance, our first impression of a face
may lead us to believe that a person is smart, worthy of our trust, and perhaps
even our admiration - regardless of the underlying truth behind such
attributes. Psychologists believe that these judgements are based on a variety
of factors such as emotional states, personality traits, and other physiognomic
cues. But work in this direction leads to an interesting question: how do we
create models for problems where there is no ground truth, only measurable
behavior? In this paper, we introduce a new convolutional neural network-based
regression framework that allows us to train predictive models of crowd
behavior for social attribute assignment. Over images from the AFLW face
database, these models demonstrate strong correlations with human crowd
ratings.
