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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2014 Vol. 4(6): 533-537 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.468

An Efficient Offline Signature Verification System

M. Arathi and A. Govardhan
Abstract—Offline signature verification is one of most challenging area of pattern recognition. Many methods have been introduced in literature to find whether a given signature is genuine or forgery. In the proposed work, the signature image is converted into time series data using linear scanning method and then time series shapelets are identified to distinguish genuine signatures from forged ones. The shapelets are time series subsequences which are maximally representative of a class. To compare the time series data, the proposed method uses Mahalanobis distance measure. The experimental results show that the method has great reduction in equal error rate.

Index Terms—Mahalanobis distance measure, time series data, time series shapelets, signatures.

The authors are with Jawaharlal Nehru Technological University Hyderabad, Hyderabad-500085, Andhra Pradesh, India (e-mail: arathi.jntu@gmail.com, govardhan_cse@yahoo.co.in).


Cite: M. Arathi and A. Govardhan, "An Efficient Offline Signature Verification System," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 533-537, 2014.

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