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Online handwritten signature verification using neural network classifier based on principal component analysis

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dc.citedby28
dc.contributor.authorIranmanesh V.en_US
dc.contributor.authorAhmad S.M.S.en_US
dc.contributor.authorAdnan W.A.W.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorArigbabu O.A.en_US
dc.contributor.authorMalallah F.L.en_US
dc.contributor.authorid56047920000en_US
dc.contributor.authorid24721182400en_US
dc.contributor.authorid6506665562en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid56047591000en_US
dc.contributor.authorid56102103900en_US
dc.date.accessioned2023-05-16T02:46:17Z
dc.date.available2023-05-16T02:46:17Z
dc.date.issued2014
dc.description.abstractOne of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%. © 2014 Vahab Iranmanesh et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo381469
dc.identifier.doi10.1155/2014/381469
dc.identifier.scopus2-s2.0-84964238181
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84964238181&doi=10.1155%2f2014%2f381469&partnerID=40&md5=ee5a30e9295f33730a9ba41bca7a59b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21959
dc.identifier.volume2014
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific World Journal
dc.titleOnline handwritten signature verification using neural network classifier based on principal component analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication
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