# Quality Measures in Biometric Systems

arXiv: https://arxiv.org/abs/2111.08704 · HTML: https://mlsanity.com/paper/2111.08704/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 34 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [A Comparative Study of Fingerprint Image-Quality Estimation Methods](https://mlsanity.com/paper/2111.07432/) (2021)

## Built on by (most cited first, 12 of 12)

- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation](https://mlsanity.com/paper/2210.13129/) (2022)
- [Toward Face Biometric De-identification using Adversarial Examples](https://mlsanity.com/paper/2302.03657/) (2023)
- [FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment](https://mlsanity.com/paper/2201.00770/) (2022)
- [An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification](https://mlsanity.com/paper/2307.13428/) (2023)
- [Comparison of Visual Trackers for Biomechanical Analysis of Running](https://mlsanity.com/paper/2505.04713/) (2025)
- [Ocular Authentication: Fusion of Gaze and Periocular Modalities](https://mlsanity.com/paper/2505.17343/) (2025)
- [A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning](https://mlsanity.com/paper/2203.14203/) (2022)
- [Forensicability Assessment of Questioned Images in Recapturing Detection](https://mlsanity.com/paper/2209.01935/) (2022)
- [Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment](https://mlsanity.com/paper/2607.22752/) (2026)
- [Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases](https://mlsanity.com/paper/2608.06380/) (2026)

## Abstract (arXiv)

Biometric technology has been increasingly deployed in the past decade, offering greater security and convenience than traditional methods of personal recognition. Although biometric signals' quality heavily affects a biometric system's performance, prior research on evaluating quality is limited. Quality is a critical issue in security, especially in adverse scenarios involving surveillance cameras, forensics, portable devices, or remote access through the Internet. This article analyzes what factors negatively impact biometric quality, how to overcome them, and how to incorporate quality measures into biometric systems. A review of the state of the art in these matters gives an overall framework for the challenges of biometric quality.