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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • 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 2012 Vol.2(5): 681-684 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.214

Online Signature System Based on Pressure and Speed Features

Nor Azlin Rosli and Azlinah Mohamed
Abstract—Online signature or dynamic signature is a biometric modality that uses, for recognition purpose, the behavioural that an individual exhibit when signing. There’s a lot of factor that can be dynamically captured in the online signature recognition as the indicator of individual’s identity or personality. This research aims to identify the two (2) important features of online signatures, pressure and speed and collaborate with the previous research in HoloCatT Matrix construction by [9]. The process involves data acquisition where the person will sign on the digital tablet and the signature is captured in an x and y values. Subsequently, an algorithm is formulated to capture the preasure and speed of the signature. Both factors have been divided into three (3) categories as reflected in the HoloCatT Matrix formulated in previous research. Five (5) signatures were captured for the functional testing and another six (6) signatures captured for the verification testing. The results from both testing show the accuracy of proposed algorithm and it has been verified by the handwriting expert by comparing the results between online and manual signature analysis.

Index Terms—Online signature, HoloCatT matrix, personality.

The authors are with Faculty of Computer Science and Mathematics, Universiti Teknologi MARA, Malaysia.


Cite:Nor Azlin Rosli and Azlinah Mohamed, "Online Signature System Based on Pressure and Speed Features," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 681-684, 2012.

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