Off-line signature verification and forgery detection using fuzzy modeling

  • Authors:

  • Madasu Hanmandlu;Mohd. Hafizuddin Mohd. Yusof;Vamsi Krishna Madasu

  • Affiliations:

  • Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India;Faculty of Information Technology, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor Darul Ehsan, Malaysia;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane QLD 4072, Australia

  • Venue:
  • Pattern Recognition
  • Year:
  • 2005

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Abstract

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance.