  Digital portrait photographs are everywhere, and while the number of face
pictures keeps growing, not much work has been done to on automatic portrait
beauty assessment. In this paper, we design a specific framework to
automatically evaluate the beauty of digital portraits. To this end, we procure
a large dataset of face images annotated not only with aesthetic scores but
also with information about the traits of the subject portrayed. We design a
set of visual features based on portrait photography literature, and
extensively analyze their relation with portrait beauty, exposing interesting
findings about what makes a portrait beautiful. We find that the beauty of a
portrait is linked to its artistic value, and independent from age, race and
gender of the subject. We also show that a classifier trained with our features
to separate beautiful portraits from non-beautiful portraits outperforms
generic aesthetic classifiers.
