Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1202
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dc.contributorDepartment of Computing-
dc.creatorYu, L-
dc.creatorWang, K-
dc.creatorZhang, DD-
dc.date.accessioned2014-12-11T08:27:18Z-
dc.date.available2014-12-11T08:27:18Z-
dc.identifier.isbn0-7803-9134-9-
dc.identifier.urihttp://hdl.handle.net/10397/1202-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2005 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.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectBox countingen_US
dc.subjectFractal dimensionen_US
dc.subjectIris imageen_US
dc.subjectCoarse classificationen_US
dc.titleCoarse iris classification based on box-counting methoden_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79% and 34.17%. The iris images are classified with the double threshold algorithm, which classifies iris images with an accuracy of 94.61%. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy, v. 3, p. 301-304-
dcterms.issued2005-
dc.identifier.scopus2-s2.0-33749246574-
dc.relation.ispartofbook2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy-
dc.relation.conferenceIEEE International Conference on Image Processing [ICIP]-
dc.identifier.rosgroupidr26238-
dc.description.ros2005-2006 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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