# State of the Art of Quality Assessment of Facial Images

arXiv: https://arxiv.org/abs/2211.08030 · HTML: https://mlsanity.com/paper/2211.08030/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2022.

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

- [NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality](https://mlsanity.com/paper/2308.09963/) (2023)
- [StableMorph: High-Quality Face Morph Generation with Stable Diffusion](https://mlsanity.com/paper/2511.08090/) (2025)
- [Quality-Aware Prototype Memory for Face Representation Learning](https://mlsanity.com/paper/2311.07734/) (2023)
- [SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems](https://mlsanity.com/paper/2507.01607/) (2025)
- [Image Quality Assessment of Identity Cards Using Measures from Open Face Image Quality](https://mlsanity.com/paper/2606.11884/) (2026)

## Abstract (arXiv)

The goal of the project "Facial Metrics for EES" is to develop, implement and publish an open source algorithm for the quality assessment of facial images (OFIQ) for face recognition, in particular for border control scenarios.1 In order to stimulate the harmonization of the requirements and practices applied for QA for facial images, the insights gained and algorithms developed in the project will be contributed to the current (2022) revision of the ISO/IEC 29794-5 standard. Furthermore, the implemented quality metrics and algorithms will consider the recommendations and requirements from other relevant standards, in particular ISO/IEC 19794-5:2011, ISO/IEC 29794-5:2010, ISO/IEC 39794-5:2019 and Version 5.2 of the BSI Technical Guideline TR-03121 Part 3 Volume 1. In order to establish an informed basis for the selection of quality metrics and the development of corresponding quality assessment algorithms, the state of the art of methods and algorithms (defining a metric), implementations and datasets for quality assessment for facial images is surveyed. For all relevant quality aspects, this document summarizes the requirements of the aforementioned standards, known results on their impact on face recognition performance, publicly available datasets, proposed methods and algorithms and open source software implementations.