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Consensus fingerprint matching with genetically optimised approach

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Title: Consensus fingerprint matching with genetically optimised approach
Authors: Sheng, W.1 weiguo.sheng@brunel.ac.uk, Howells, G.2 w.g.j.howells@kent.ac.uk, Fairhurst, M.C.2 m.c.fairhurst@kent.ac.uk, Deravi, F.2 f.deravi@kent.ac.uk, Harmer, K.2 k.harmer@kent.ac.uk
Source: Pattern Recognition. Jul2009, Vol. 42 Issue 7, p1399-1407. 9p.
Subjects: Human fingerprints, Genetic algorithms, Pattern perception, Robust control, Combinatorics
Abstract: Abstract: Fingerprint matching has been approached using various criteria based on different extracted features. However, robust and accurate fingerprint matching is still a challenging problem. In this paper, we propose an improved integrated method which operates by first suggesting a consensus matching function, which combines different matching criteria based on heterogeneous features. We then devise a genetically guided approach to optimise the consensus matching function for simultaneous fingerprint alignment and verification. Since different features usually offer complementary information about the matching task, the consensus function is expected to improve the reliability of fingerprint matching. A related motivation for proposing such a function is to build a robust criterion that can perform well over a variety of different fingerprint matching instances. Additionally, by employing the global search functionality of a genetic algorithm along with a local matching operation for population initialisation, we aim to identify the optimal or near optimal global alignment between two fingerprints. The proposed algorithm is evaluated by means of a series of experiments conducted on public domain collections of fingerprint images and compared with previous work. Experimental results show that the consensus function can lead to a substantial improvement in performance while the local matching operation helps to identify promising initial alignment configurations, thereby speeding up the verification process. The resulting algorithm is more accurate than several other proposed methods which have been implemented for comparison. [Copyright &y& Elsevier]
Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 37157539
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  Data: <searchLink fieldCode="AR" term="%22Sheng%2C+W%2E%22">Sheng, W.</searchLink><relatesTo>1</relatesTo><i> weiguo.sheng@brunel.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Howells%2C+G%2E%22">Howells, G.</searchLink><relatesTo>2</relatesTo><i> w.g.j.howells@kent.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Fairhurst%2C+M%2EC%2E%22">Fairhurst, M.C.</searchLink><relatesTo>2</relatesTo><i> m.c.fairhurst@kent.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Deravi%2C+F%2E%22">Deravi, F.</searchLink><relatesTo>2</relatesTo><i> f.deravi@kent.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Harmer%2C+K%2E%22">Harmer, K.</searchLink><relatesTo>2</relatesTo><i> k.harmer@kent.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Jul2009, Vol. 42 Issue 7, p1399-1407. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Human+fingerprints%22">Human fingerprints</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorics%22">Combinatorics</searchLink>
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  Data: Abstract: Fingerprint matching has been approached using various criteria based on different extracted features. However, robust and accurate fingerprint matching is still a challenging problem. In this paper, we propose an improved integrated method which operates by first suggesting a consensus matching function, which combines different matching criteria based on heterogeneous features. We then devise a genetically guided approach to optimise the consensus matching function for simultaneous fingerprint alignment and verification. Since different features usually offer complementary information about the matching task, the consensus function is expected to improve the reliability of fingerprint matching. A related motivation for proposing such a function is to build a robust criterion that can perform well over a variety of different fingerprint matching instances. Additionally, by employing the global search functionality of a genetic algorithm along with a local matching operation for population initialisation, we aim to identify the optimal or near optimal global alignment between two fingerprints. The proposed algorithm is evaluated by means of a series of experiments conducted on public domain collections of fingerprint images and compared with previous work. Experimental results show that the consensus function can lead to a substantial improvement in performance while the local matching operation helps to identify promising initial alignment configurations, thereby speeding up the verification process. The resulting algorithm is more accurate than several other proposed methods which have been implemented for comparison. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.patcog.2008.11.038
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      – SubjectFull: Pattern perception
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      – TitleFull: Consensus fingerprint matching with genetically optimised approach
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              Text: Jul2009
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