# Aesthetics Assessment of Images Containing Faces

arXiv: https://arxiv.org/abs/1805.08685 · HTML: https://mlsanity.com/paper/1805.08685/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 3 of 3)

- [Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels](https://mlsanity.com/paper/2312.03222/) (2023)
- [Neural Image Beauty Predictor Based on Bradley-Terry Model](https://mlsanity.com/paper/2111.10127/) (2021)
- [Automated Assessment of Aesthetic Outcomes in Facial Plastic Surgery](https://mlsanity.com/paper/2508.13363/) (2025)

## Abstract (arXiv)

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.