Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
Hartwig Fronthaler, Klaus Kollreider, Josef Bigun, Julian Fierrez,, Fernando Alonso-Fernandez, Javier Ortega-Garcia, Joaquin Gonzalez-Rodriguez

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
