# FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance

arXiv: https://arxiv.org/abs/2111.02078 · HTML: https://mlsanity.com/paper/2111.02078/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Biometric Quality: Review and Application to Face Recognition with FaceQnet](https://mlsanity.com/paper/2006.03298/) (2020)

## Built on by (most cited first, 8 of 8)

- [Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma](https://mlsanity.com/paper/2304.13680/) (2023)
- [FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment](https://mlsanity.com/paper/2201.00770/) (2022)
- [Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance](https://mlsanity.com/paper/2504.18886/) (2025)
- [Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics](https://mlsanity.com/paper/2506.10564/) (2025)
- [Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality](https://mlsanity.com/paper/2309.04022/) (2023)
- [Eye Sclera for Fair Face Image Quality Assessment](https://mlsanity.com/paper/2501.07158/) (2025)
- [Radial Distortion in Face Images: Detection and Impact](https://mlsanity.com/paper/2501.07179/) (2025)
- [DFIC: Towards a balanced facial image dataset for automatic ICAO compliance verification](https://mlsanity.com/paper/2602.10985/) (2026)

## Abstract (arXiv)

In this paper we develop FaceQvec, a software component for estimating the conformity of facial images with each of the points contemplated in the ISO/IEC 19794-5, a quality standard that defines general quality guidelines for face images that would make them acceptable or unacceptable for use in official documents such as passports or ID cards. This type of tool for quality assessment can help to improve the accuracy of face recognition, as well as to identify which factors are affecting the quality of a given face image and to take actions to eliminate or reduce those factors, e.g., with postprocessing techniques or re-acquisition of the image. FaceQvec consists of the automation of 25 individual tests related to different points contemplated in the aforementioned standard, as well as other characteristics of the images that have been considered to be related to facial quality. We first include the results of the quality tests evaluated on a development dataset captured under realistic conditions. We used those results to adjust the decision threshold of each test. Then we checked again their accuracy on a evaluation database that contains new face images not seen during development. The evaluation results demonstrate the accuracy of the individual tests for checking compliance with ISO/IEC 19794-5. FaceQvec is available online (https://github.com/uam-biometrics/FaceQvec).