A Comparative Study of Fingerprint Image-Quality Estimation Methods

TL;DR

This paper reviews and tests various fingerprint image-quality estimation methods, highlighting their behaviors and impact on verification performance using a large, multi-sensor dataset.

Contribution

It provides a comprehensive comparison of existing fingerprint quality measures and evaluates their influence on fingerprint verification accuracy.

Findings

High correlation among different quality measures.

Low-quality images significantly degrade verification performance.

The study offers insights into the robustness of quality estimation methods.

Abstract

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.