  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.
