Oriented diffusion filtering for enhancing low-quality fingerprint images

C. Gottschlich, C.-B. Schönlieb

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors' new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors' knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download. © 2012 The Institution of Engineering and Technology.
Original languageEnglish (US)
Pages (from-to)105
JournalIET Biometrics
Issue number2
StatePublished - 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-I1-007-43
Acknowledgements: The authors thank Thomas Hotz, Stephan Huckemann and Axel Munk for their valuable comments during the preparation of this manuscript. C. Gottschlich and C.-B. Schonlieb gratefully acknowledge support by DFG RTG 1023 'Identification in Mathematical Models: Synergy of Stochastic and Numerical Methods'. Moreover, C.-B. Schonlieb acknowledges the financial support provided by the project WWTF Five senses-Call 2006, 'Mathematical Methods for Image Analysis and Processing in the Visual Arts' and the 'Cambridge Centre for Analysis' (CCA). Further, this publication is based on work supported by Award No. KUK-I1-007-43, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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