Signature Verification Using Support VectorMachine(SVM)

Journal Title: International Journal of Scientific Research and Management - Year 2017, Vol 5, Issue 5

Abstract

Automated signature verificationhas many applications in our daily lifelikeBank-cheque processing,document authentication, ATM access etc. Handwritten signatures have proved to be important inauthenticating a person's identity, who is signing the document.In this paper we present an off-linesignature verification and recognition system using the global, directional and grid features of signatures.Support vector machine (SVM) was used to verify and classify the signatures.As there are unique andimportant variations in the feature elements of each signature, so in orderto match a particular signaturewith the database, the structural parameters of the signatures along with the local variations in thesignature characteristics are used.The artificial neural networks are trained by these characteristic. Thesystem uses the features extracted from the signatures such as centroid, height–width ratio, total area,first and second order derivatives, quadrant areas etc. After the verification of the signature the anglefeatures are used in fuzzy logic based system for forgerydetection and theperformance is increasesapproximately (80%) when usingSVM as a classifier

Authors and Affiliations

Ravinder kumar

Keywords

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  • EP ID EP312937
  • DOI -
  • Views 55
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How To Cite

Ravinder kumar (2017). Signature Verification Using Support VectorMachine(SVM). International Journal of Scientific Research and Management, 5(5), -. https://europub.co.uk/articles/-A-312937