# A Comparative Study of Fingerprint Image-Quality Estimation Methods

arXiv: https://arxiv.org/abs/2111.07432 · HTML: https://mlsanity.com/paper/2111.07432/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 36 references that are in the map.
- First posted on arXiv 14 Nov 2021.

## Built on by (most cited first, 9 of 9)

- [Quality Measures in Biometric Systems](https://mlsanity.com/paper/2111.08704/) (2021)
- [Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms](https://mlsanity.com/paper/2111.08703/) (2021)
- [Evaluation of direct attacks to fingerprint verification systems](https://mlsanity.com/paper/2212.07575/) (2022)
- [Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability](https://mlsanity.com/paper/2211.13554/) (2022)
- [Fingerprint Liveness Detection Based on Quality Measures](https://mlsanity.com/paper/2207.04809/) (2022)
- [Forensicability Assessment of Questioned Images in Recapturing Detection](https://mlsanity.com/paper/2209.01935/) (2022)
- [UFQA: Utility guided Fingerphoto Quality Assessment](https://mlsanity.com/paper/2407.11141/) (2024)
- [Illumination-Aware Contactless Fingerprint Spoof Detection via Paired Flash-Non-Flash Imaging](https://mlsanity.com/paper/2603.17679/) (2026)
- [Infant Fingerprints: Quality Assessment & Recognition](https://mlsanity.com/paper/2610.20432/) (2026)

## Abstract (arXiv)

One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images. In this work, we review existing approaches for fingerprint image-quality estimation, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions. We have also tested a selection of fingerprint image-quality estimation algorithms. For the experiments, we employ the BioSec multimodal baseline corpus, which includes 19200 fingerprint images from 200 individuals acquired in two sessions with three different sensors. The behavior of the selected quality measures is compared, showing high correlation between them in most cases. The effect of low-quality samples in the verification performance is also studied for a widely available minutiae-based fingerprint matching system.