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Article: Improvement of fingerprint retrieval by a statistical classifier

TitleImprovement of fingerprint retrieval by a statistical classifier
Authors
KeywordsDistorted sample
fingerprint identification
fingerprint retrieval
FVC database
NIST-4 database
Issue Date2011
PublisherI E E E. The Journal's web site is located at http://www.ieee.org/organizations/society/sp/tifs.html
Citation
Ieee Transactions On Information Forensics And Security, 2011, v. 6 n. 1, p. 59-69 How to Cite?
AbstractThe topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple $k$-nearest neighbor ($k$-NN) classification. However, the $k$-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-$N$ fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/139266
ISSN
2015 Impact Factor: 2.441
2015 SCImago Journal Rankings: 1.860
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, KCen_HK
dc.contributor.authorLeung, CHen_HK
dc.date.accessioned2011-09-23T05:47:46Z-
dc.date.available2011-09-23T05:47:46Z-
dc.date.issued2011en_HK
dc.identifier.citationIeee Transactions On Information Forensics And Security, 2011, v. 6 n. 1, p. 59-69en_HK
dc.identifier.issn1556-6013en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139266-
dc.description.abstractThe topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple $k$-nearest neighbor ($k$-NN) classification. However, the $k$-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-$N$ fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://www.ieee.org/organizations/society/sp/tifs.htmlen_HK
dc.relation.ispartofIEEE Transactions on Information Forensics and Securityen_HK
dc.rightsIEEE Transactions on Information Forensics and Security. Copyright © IEEE.-
dc.rights©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectDistorted sampleen_HK
dc.subjectfingerprint identificationen_HK
dc.subjectfingerprint retrievalen_HK
dc.subjectFVC databaseen_HK
dc.subjectNIST-4 databaseen_HK
dc.titleImprovement of fingerprint retrieval by a statistical classifieren_HK
dc.typeArticleen_HK
dc.identifier.emailLeung, KC:kcleung@eee.hku.hken_HK
dc.identifier.emailLeung, CH:chleung@eee.hku.hken_HK
dc.identifier.authorityLeung, KC=rp00147en_HK
dc.identifier.authorityLeung, CH=rp00146en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TIFS.2010.2100382en_HK
dc.identifier.scopuseid_2-s2.0-79951835585en_HK
dc.identifier.hkuros194879en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79951835585&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6en_HK
dc.identifier.issue1en_HK
dc.identifier.spage59en_HK
dc.identifier.epage69en_HK
dc.identifier.eissn1556-6021-
dc.identifier.isiWOS:000287409400007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLeung, KC=7401860663en_HK
dc.identifier.scopusauthoridLeung, CH=7402612415en_HK

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