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

Institute of Signal Processing

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.

Original language	English
Title of host publication	2011 International Conference on Digital Image Computing: Techniques and Applications
Number of pages	7
Publisher	IEEE
Publication date	01.12.2011
Pages	487-493
Article number	6128708
ISBN (Print)	978-1-4577-2006-2
ISBN (Electronic)	978-0-7695-4588-2
DOIs	https://doi.org/10.1109/DICTA.2011.88
Publication status	Published - 01.12.2011
Event	2011 International Conference on Digital Image Computing: Techniques and Applications - Noosa, Australia Duration: 06.12.2011 → 08.12.2011 Conference number: 88414

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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, pp. 487-493, 2011 International Conference on Digital Image Computing: Techniques and Applications, Noosa, Queensland, Australia, 06.12.11. https://doi.org/10.1109/DICTA.2011.88

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

Abstract

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