文章基本信息

标题：Online Signature Verification Using Energy, Angle and Directional Gradient Feature With Neural Network
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作者：PRIYANK JAIN ; JAYESH GANGRADE
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2013
卷号：2
期号：9
页码：4655
出版社：S&S Publications
摘要：Signature used as a biometric is implemented in various systems as well as every signature signed by eachperson is distinct at the same time. It is very important to have anonline computerized signature Verification systemdifferentiate digital signature. Hand written signature used every day at various places (Bank, Office etc) for theauthentication of a person, but a signature of a person may not be same at different time or it may be generated by somefraud way. So therobust system is required for verification of the signature. The signature verification can be doneeither online or offline, here we are using online signature verification network. In the proposed system the signaturesis taking as a image by the signature pad and apply image processing technique before the feature extraction to makethe system effective. The angle, energy and chain code features are used in this paper to differentiate the signature.Neural network is used as a classifier for this system. The studies of online signature verification are given in thispaper.
关键词：Directional Feature; Energy Density; Chain Code; Neural Network.