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Online handwritten signature verification via network analysis

Author

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  • Santos, Yan Antonino Costa
  • Rêgo, Leandro Chaves
  • Ospina, Raydonal

Abstract

In this paper, a model for online handwritten signature verification via networks analysis is proposed, in which centrality metrics are used as predictive characteristics. Supervised learning methods were used to classify the veracity of the signatures. The study uses the MCYT-100 and SVC2004 task2 signature databases, which are widely used in signature verification problems. In the framework, signatures were considered as nodes of a complex network in which the existing connection relationship between two signatures is measured through Pearson correlations between time series of signature coordinates that exceed a threshold of comparison. The attributes for the model were topological measures of centralities of the networks and the entropy of the time series signature. Some metrics are used to measure classification errors, including the False Rejection Rate (FRR) which identifies the percentage of true signatures that are rejected by the model, the False Acceptance Rate (FAR) as the percentage of false signatures that are accepted by the learning engine and the Average Error Rate (AER) as the average error. In this framework, we obtained a false positive rate of 6.19%, a false negative rate of 6.39% and an average error of approximately 6.28%. This result is close to ones obtained by state-of-the-art models for this problem, showing that the use of complex networks for online handwritten signature verification is a promising venue of research.

Suggested Citation

  • Santos, Yan Antonino Costa & Rêgo, Leandro Chaves & Ospina, Raydonal, 2022. "Online handwritten signature verification via network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    • Handle: RePEc:eee:phsmap:v:600:y:2022:i:c:s0378437122004022
    DOI: 10.1016/j.physa.2022.127582
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    References listed on IDEAS

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    1. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    2. Osvaldo A Rosso & Raydonal Ospina & Alejandro C Frery, 2016. "Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-19, December.
    3. Osvaldo Rosso & Felipe Olivares & Luciano Zunino & Luciana Micco & André Aquino & Angelo Plastino & Hilda Larrondo, 2013. "Characterization of chaotic maps using the permutation Bandt-Pompe probability distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(4), pages 1-13, April.
    4. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
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