Aesthetics Assessment of Images Containing Faces

TL;DR

This paper presents a novel deep learning approach for assessing the aesthetic quality of images containing faces, outperforming existing methods across multiple datasets.

Contribution

It introduces a multi-CNN framework that combines perceptual, aesthetic, and facial features specifically for face images, advancing face-specific aesthetic assessment.

Findings

Outperforms existing methods on four datasets

Effective in both binary and continuous aesthetic prediction

Utilizes three specialized CNNs for feature encoding

Abstract

Recent research has widely explored the problem of aesthetics assessment of images with generic content. However, few approaches have been specifically designed to predict the aesthetic quality of images containing human faces, which make up a massive portion of photos in the web. This paper introduces a method for aesthetic quality assessment of images with faces. We exploit three different Convolutional Neural Networks to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e. low/high, and continuous aesthetic score prediction on four different databases in the state-of-the-art.