Aesthetic quality assessment of photos with faces

Abstract

Aesthetic quality assessment of photos is a challenging task regarding the complexity of various photos and subjectivity of human’s aesthetic perception. Recent research has suggested that photos with different contents have different aesthetic characters. However, these different aesthetic characters are not considered in previous work of aesthetic assessment. Meanwhile, photos with human faces have become increasingly popular and constitute an important part of social photo collections. In this work, we analyze the characters of this particular category of photos, human faces, and study the impact of them on aesthetic quality estimation. This study could have many potential applications, including selection of high aesthetic face photos, face photo editing and so on. To solve this problem, we design new handcrafted features and fine-tuned a new deep Convolutional Neural Network (CNN) for features. Next, we build decision fusion model to employ all the proposed features for aesthetic estimation. In addition, we analyze the effectiveness of different groups of features in a face photo classification task to better understand their differences. Experimental results show that our proposed features are effective and the classifier outperforms several up-to-date approaches in aesthetic quality assessment.