Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification

TL;DR

This paper investigates fingerprint image quality assessment using orientation tensors and symmetry descriptors, enhancing recognition accuracy and multialgorithm fusion through quality-aware strategies and novel fusion schemes.

Contribution

It introduces a new quality estimation method based on orientation tensors and symmetry descriptors, and develops fusion strategies that incorporate quality measures for improved fingerprint recognition.

Findings

Quality awareness boosts recognition rates.

Fusion schemes incorporating quality measures outperform traditional methods.

Proposed methods are effective across multiple public databases.

Abstract

Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained score-level fusion schemes are investigated. A Bayes-based strategy for incorporating experts past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, is presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).