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Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
Univ Autonoma Madrid, Escuela Politec Super, ATVS, Madrid, Spain.
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2008 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 3, no 2, 331-338 p.Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
New York, N.Y.: IEEE Signal Processing Society, 2008. Vol. 3, no 2, 331-338 p.
Keyword [en]
adaptive fusion, Bayesian statistics, cascaded fusion, fingerprint identification, monomodal fusion, quality assessment, structure tensor, symmetry features, Bayes methods, image fusion, image resolution
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-2064DOI: 10.1109/TIFS.2008.920725ISI: 000257824200017Scopus ID: 2-s2.0-44049092040Local ID: 2082/2459OAI: oai:DiVA.org:hh-2064DiVA: diva2:239282
Note

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Available from: 2008-10-18 Created: 2008-10-18 Last updated: 2015-09-29Bibliographically approved

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Fronthaler, HartwigKollreider, KlausBigun, JosefAlonso-Fernandez, Fernando
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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  • Other locale
More languages
Output format
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