Feature design for aesthetic inference on photos with faces

Determining the aesthetics of photographs has recently become a research topic of considerable interest. In this project, we focus on constructing meaningful features to model the aesthetic quality of photos with faces. Utilizing face information, color, composition features, as well as novel saliency-based spatial features, we construct an aesthetic inference model, which is more accurate than a state-of-the-art method. Further, we show that this model can be improved by applying different sets of features for single-face and multiple-face photos. Third, we demonstrate by combining low-level generic features with handcrafted features, that the model can be made to achieve even lower error rates.