Computation of local orientations is a primary step in fingerprint recognition. A large number of approaches have been proposed in the literature, but no systematic quantitative evaluations have been done yet. We implemented and tested several well know methods and a plethora of their variants over a novel, specifically designed, benchmark, made available in the FVC-onGoing framework. We proved that parameter optimizations, pre- and post-processing stages can markedly improve accuracy of the baseline methods on bad quality fingerprints. Finally, in this paper we propose a novel adaptive method which selectively exploits accuracy of local-based analysis and learning-based global methods, thus achieving the overall best performance on a challenging dataset.

Improving Fingerprint Orientation Extraction

TURRONI, FRANCESCO;MALTONI, DAVIDE;CAPPELLI, RAFFAELE;MAIO, DARIO
2011

Abstract

Computation of local orientations is a primary step in fingerprint recognition. A large number of approaches have been proposed in the literature, but no systematic quantitative evaluations have been done yet. We implemented and tested several well know methods and a plethora of their variants over a novel, specifically designed, benchmark, made available in the FVC-onGoing framework. We proved that parameter optimizations, pre- and post-processing stages can markedly improve accuracy of the baseline methods on bad quality fingerprints. Finally, in this paper we propose a novel adaptive method which selectively exploits accuracy of local-based analysis and learning-based global methods, thus achieving the overall best performance on a challenging dataset.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/107939
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