# The Beauty of Capturing Faces: Rating the Quality of Digital Portraits

arXiv: https://arxiv.org/abs/1501.07304 · HTML: https://mlsanity.com/paper/1501.07304/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2015.

## Built on by (most cited first, 5 of 5)

- [An Image Quality Assessment Dataset for Portraits](https://mlsanity.com/paper/2304.05772/) (2023)
- [Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey](https://mlsanity.com/paper/2404.11159/) (2024)
- [A Survey on Image Aesthetic Assessment](https://mlsanity.com/paper/2103.11616/) (2021)
- [Detecting Cultural Differences in News Video Thumbnails via Computational Aesthetics](https://mlsanity.com/paper/2505.21912/) (2025)
- [PortraitAes: Intent-Conditioned Structured Portrait Aesthetic Assessment](https://mlsanity.com/paper/2610.05010/) (2026)

## Abstract (arXiv)

Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment. In this paper, we design a specific framework to automatically evaluate the beauty of digital portraits. To this end, we procure a large dataset of face images annotated not only with aesthetic scores but also with information about the traits of the subject portrayed. We design a set of visual features based on portrait photography literature, and extensively analyze their relation with portrait beauty, exposing interesting findings about what makes a portrait beautiful. We find that the beauty of a portrait is linked to its artistic value, and independent from age, race and gender of the subject. We also show that a classifier trained with our features to separate beautiful portraits from non-beautiful portraits outperforms generic aesthetic classifiers.