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.
F. Turroni, D. Maltoni, R. Cappelli, D. Maio (2011). Improving Fingerprint Orientation Extraction. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 6, 1002-1013 [10.1109/TIFS.2011.2150216].
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
