Publications:Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification


Do not edit this section

Keep all hand-made modifications below

Title Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
Author Hartwig Fronthaler and Klaus Kollreider and Josef Bigun and Julian Fierrez and Fernando Alonso-Fernandez and Javier Ortega-Garcia and Joaquin Gonzalez-Rodriguez
Year 2008
PublicationType Journal Paper
Journal IEEE Transactions on Information Forensics and Security
Diva url
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).