Robust Core-Point-ROI Based Fingerprint Identification Using a Sparse Classifier

A. P. Condurache; A. Mertins

doi:10.1109/DICTA.2011.88

Robust Core-Point-ROI Based Fingerprint Identification Using a Sparse Classifier

A. P. Condurache, A. Mertins

Institut für Signalverarbeitung

Abstract

We address the problem of automated fingerprints-based person identification from poor-quality fingerprints. Our solution includes the definition of a region of interest centered over the fingerprint at a reference point. From this region of interest a feature vector is computed that is invariant to some geometrical transforms but also to point transforms of the gray levels in the region of interest. This feature vector is then classified by means of a sparse classifier. We successfully test our algorithms on a publicly available fingerprints database and show that they are robust to a set of issues afflicting current fingerprint-identification systems in the case of poor-quality fingerprints.

Originalsprache	Englisch
Titel	2011 International Conference on Digital Image Computing: Techniques and Applications
Seitenumfang	7
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	01.12.2011
Seiten	487-493
Aufsatznummer	6128708
ISBN (Print)	978-1-4577-2006-2
ISBN (elektronisch)	978-0-7695-4588-2
DOIs	https://doi.org/10.1109/DICTA.2011.88
Publikationsstatus	Veröffentlicht - 01.12.2011
Veranstaltung	2011 International Conference on Digital Image Computing: Techniques and Applications - Noosa, Australien Dauer: 06.12.2011 → 08.12.2011 Konferenznummer: 88414

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Dokumente

10.1109/DICTA.2011.88

Weitere Links

https://www.scopus.com/record/display.uri?eid=2-s2.0-84856982754&origin=inward&txGid=7ebfe325b78ce084fb64c6b518ee3e61

Zitieren

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title = "Robust Core-Point-ROI Based Fingerprint Identification Using a Sparse Classifier",

abstract = "We address the problem of automated fingerprints-based person identification from poor-quality fingerprints. Our solution includes the definition of a region of interest centered over the fingerprint at a reference point. From this region of interest a feature vector is computed that is invariant to some geometrical transforms but also to point transforms of the gray levels in the region of interest. This feature vector is then classified by means of a sparse classifier. We successfully test our algorithms on a publicly available fingerprints database and show that they are robust to a set of issues afflicting current fingerprint-identification systems in the case of poor-quality fingerprints.",

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Condurache, AP & Mertins, A 2011, Robust Core-Point-ROI Based Fingerprint Identification Using a Sparse Classifier. in 2011 International Conference on Digital Image Computing: Techniques and Applications., 6128708, IEEE Proceedings , IEEE, S. 487-493, 2011 International Conference on Digital Image Computing: Techniques and Applications, Noosa, Queensland, Australien, 06.12.11. https://doi.org/10.1109/DICTA.2011.88

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