Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30695
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorLi, Y-
dc.creatorWang, K-
dc.creatorLi, T-
dc.creatorZhang, D-
dc.date.accessioned2014-12-31T08:01:49Z-
dc.date.available2014-12-31T08:01:49Z-
dc.identifier.issn1002-0470-
dc.identifier.urihttp://hdl.handle.net/10397/30695-
dc.language.isozhen_US
dc.publisher中国学术期刊(光盘版)电子杂志社en_US
dc.subjectModular neural network (MNN)en_US
dc.subjectPalmprint recognitionen_US
dc.subjectTranslation invariant Zernike momentsen_US
dc.titlePalmprint recognition based on translation invariant Zernike moments and modular neural networken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage19-
dc.identifier.epage23-
dc.identifier.volume15-
dc.identifier.issue12-
dcterms.abstractThis paper introduces a new approach for palmprint recognition, using translation invariant Zernike moments (TIZMs) as palm features, and a modular neural network (MNN) as classifier. Translation invariance is added to the general Zernike moments which have a good property of rotation invariance. The pattern set is set up by eight-order TIZMs with 25 dimensions. A modular neural network is presented in order to decompose the palmprint recognition task into a series of smaller and simpler two-class sub-problems. Simulations have been done on the Polyu_PalmprintDB database, which is composed of 3200 palmprints (10 palmprints/person). Experimental results demonstrate that higher identification rate and recognition rate are achieved by the proposed method in contrast with the straight-line segments (SLS) based method and the Fuzzy Directional Element Energy Feature (FDEEF) method.-
dcterms.bibliographicCitation高技术通讯 (High technology letters), 2005, v. 15, no. 12, p. 19-23-
dcterms.isPartOf高技术通讯 (High technology letters)-
dcterms.issued2005-
dc.identifier.scopus2-s2.0-30544441909-
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