Rough set approach to online signature identification

  • Authors:
  • Waheeda Al-Mayyan;Hala S. Own;Hussein Zedan

  • Affiliations:
  • Software Technology Research Laboratory, De Montfort University, Leicester, United Kingdom;Department of Solar and Space Research, National Research Institute of Astronomy and Geophysics, Cairo, Egypt;Software Technology Research Laboratory, De Montfort University, Leicester, United Kingdom

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2011

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Abstract

This paper presents an online signature identification system based on global features. The information is extracted as time functions of various dynamic properties of the signatures. A database of 2160 signatures from 108 subjects was built. Thirty-one features were identified and extracted from each signature. Different feature reduction approaches and classifiers were used to assess their suitability for this application. Rough set approach has resulted in a reduced set of nine features that were found to capture the essential characteristics required for signature identification. Rough set classifier has achieved 100% correct classification rate, which demonstrates its suitability and effectiveness for online signature identification.