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Enhancement of Fingerprint Images by Shape-Adapted Scale-Space Operators
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-9081-2170
1996 (English)In: Gaussian Scale-Space Theory. Part I: Proceedings of PhD School on Scale-Space Theory (Copenhagen, Denmark) May 1996 / [ed] J. Sporring, M. Nielsen, L. Florack, and P. Johansen, Springer Science+Business Media B.V., 1996, 21-30 p.Chapter in book (Refereed)
Abstract [en]

This work presents a novel technique for preprocessing fingerprint images. The method is based on the measurements of second moment descriptors and shape adaptation of scale-space operators with automatic scale selection (Lindeberg 1994). This procedure, which has been successfully used in the context of shape-from-texture and shape from disparity gradients, has several advantages when applied to fingerprint image enhancement, as observed by (Weickert 1995). For example, it is capable of joining interrupted ridges, and enforces continuity of their directional fields.

In this work, these abovementioned general ideas are applied and extended in the following ways: Two methods for estimating local ridge width are explored and tuned to the problem of fingerprint enhancement. A ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model. This information is used for guiding a scale-selection mechanism, and for spreading the results of shape adaptation into noisy areas.

The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. To a large extent, the scheme has the desirable property of joining interrupted lines without destroying essential singularities such as branching points. Thus, the result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image.

A detailed experimental evaluation is presented, including a comparison with other techniques. We propose that the techniques presented provide mechanisms of interest to developers of automatic fingerprint identification systems.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 1996. 21-30 p.
, Computational Imaging and Vision, ISSN 1381-6446 ; 8
National Category
Computer and Information Science Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:kth:diva-40142DOI: 10.1007/978-94-015-8802-7_2ISBN: 978-94-015-8802-7OAI: diva2:453329

QC 20111102

Available from: 2011-11-02 Created: 2011-09-13 Last updated: 2015-06-18Bibliographically approved

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