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Signature identification by Minkowski dimension.

Semyon S Rudyi1, Tatiana A Vovk1, Yuri V Rozhdestvensky1

  • 1Information Optical Technologies Center, ITMO University, 14 Birzhevaya line, Saint Petersburg, 199034, Russia.

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Summary

Signature identification is possible using the fractal Minkowski dimension, a method distinguishing original from forged documents. This approach, combined with machine learning, offers a powerful tool for verifying signatures and notations.

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Area of Science:

  • Forensic Science
  • Applied Mathematics
  • Physics

Background:

  • Signature verification is crucial for security and authenticity.
  • Existing methods face challenges with sophisticated forgeries.
  • A novel approach is needed to enhance signature identification accuracy.

Purpose of the Study:

  • To investigate the fractal Minkowski dimension as a novel parameter for signature identification.
  • To determine if this dimension can differentiate between genuine and forged signatures.
  • To explore the integration of this method with machine learning for robust verification.

Main Methods:

  • Modeling signatures as pen-tip trajectories governed by Langevin equations.
  • Calculating the fractal Minkowski dimension for various signatures.
  • Applying machine learning algorithms to the calculated dimensions for classification.

Main Results:

  • The fractal Minkowski dimension exhibits distinct values for original and falsified signatures.
  • This parameter effectively distinguishes between signatures from different individuals.
  • The combined approach demonstrates high potential for accurate signature verification.

Conclusions:

  • Fractal Minkowski dimension is a viable and effective metric for signature identification.
  • The proposed method offers a promising, data-driven approach to combat signature fraud.
  • This technique can be extended to the verification of other forms of written notations.