The Beauty of Capturing Faces: Rating the Quality of Digital Portraits
This addresses the need for automated portrait beauty evaluation in photography and social media, though it is incremental as it builds on existing aesthetic assessment work.
The authors tackled the problem of automatically assessing the beauty of digital portraits by designing a framework with specific visual features and a large annotated dataset, resulting in a classifier that outperforms generic aesthetic methods.
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.