Aesthetics Assessment of Images Containing Faces release_6flm7vsimvhzbi5qqrijuffzp4

by Simone Bianco, Luigi Celona, Raimondo Schettini

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2018  

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.
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Type  article
Stage   submitted
Date   2018-05-22
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Language   en ?
arXiv  1805.08685v1
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