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Dynamic signatures: a mathematical approach to analysis.

Jessica Baleiro Okado1, Erick Simões da Camara E Silva2, Priscila Dias Sily2

  • 1Institute of Criminalistics, Superintendence of the Technical-Scientific Police, Team Santos, São Paulo, Brazil.

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This study introduces mathematical tools like principal component analysis and dynamic time warping to objectively analyze dynamic signatures, improving handwriting examination accuracy for genuine and simulated samples.

Keywords:
data analysisdigitally captured signaturesdynamic signaturedynamic time warpingforensic handwriting examination

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Area of Science:

  • Forensic Science
  • Data Analysis
  • Biometrics

Background:

  • Handwriting examination traditionally relies on subjective analysis.
  • There is a need for objective and reproducible methods in forensic document examination.
  • Dynamic signature analysis offers a rich source of data beyond static features.

Purpose of the Study:

  • To evaluate mathematical tools for objective analysis of dynamic signatures.
  • To reduce subjectivity and enhance reproducibility in handwriting examination.
  • To assess the accuracy of a two-step mathematical approach for signature verification.

Main Methods:

  • Utilized principal component analysis (PCA) for global signature data.
  • Employed dynamic time warping (DTW) for local signature characteristic analysis.
  • Applied Kolmogorov-Smirnov hypothesis testing with optimized P-value threshold (1x10^-10).

Main Results:

  • Achieved high accuracy rates: 96.7% for genuine global data, 88.9% for simulated global data.
  • Attained average accuracy of 95.4% for genuine local data, 94.7% for simulated local data.
  • The method showed limitations with disguised signatures, aligning with traditional examination challenges.

Conclusions:

  • The proposed two-step mathematical approach significantly enhances objectivity and reproducibility in signature analysis.
  • The method demonstrates high accuracy for genuine and simulated signatures, suitable for forensic applications.
  • Expert visualization and analysis remain crucial alongside automated tools.