MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence

TL;DR

MiDeCon introduces an unsupervised method for assessing fingerprint and minutia quality based on detection confidence, improving accuracy without requiring quality labels and outperforming existing quality assessment tools.

Contribution

It presents a novel, label-free approach to evaluate fingerprint quality through minutia detection confidence applicable to any deep learning extractor.

Findings

MiDeCon outperforms NFIQ1 and NFIQ2 in quality assessment accuracy.

The method is applicable to various deep learning minutia extractors.

Experiments on FVC 2006 show significant improvements over baselines.

Abstract

An essential factor to achieve high accuracies in fingerprint recognition systems is the quality of its samples. Previous works mainly proposed supervised solutions based on image properties that neglects the minutiae extraction process, despite that most fingerprint recognition techniques are based on detected minutiae. Consequently, a fingerprint image might be assigned a high quality even if the utilized minutia extractor produces unreliable information. In this work, we propose a novel concept of assessing minutia and fingerprint quality based on minutia detection confidence (MiDeCon). MiDeCon can be applied to an arbitrary deep learning based minutia extractor and does not require quality labels for learning. We propose using the detection reliability of the extracted minutia as its quality indicator. By combining the highest minutia qualities, MiDeCon also accurately determines the quality of a full fingerprint. Experiments are conducted on the publicly available databases of the FVC 2006 and compared against several baselines, such as NIST's widely-used fingerprint image quality software NFIQ1 and NFIQ2. The results demonstrate a significantly stronger quality assessment performance of the proposed MiDeCon-qualities as related works on both, minutia- and fingerprint-level. The implementation is publicly available.