A Secure Method for Authenticity Verification of Handwritten Signatures Through Digital Image Processing and Artificial Neural Networks

Authors

  • Deivison Pinheiro Franco University of Amazônia.
  • Felipe Dantas Barboza
  • Nágila Magalhães Cardoso

DOI:

https://doi.org/10.17762/ijcnis.v5i2.382

Abstract

This paper aims to propose a method to do authenticity verification of handwritten signatures based on the use of digital image processing and artificial neural networks techniques through the backpropagation learning algorithm with 500 and 901 approaches, in order to optimize this verification process and act as a decision support tool, in an automated way. The results showed an average percentage error of 20% in the first and of 5.83% in the second, while the performance of a trained professional for that has an average error of 6.67%. Thus, we could observe the efficiency of the proposed method, as well as the difference and evolution of approaches through the relevance of the results.

Author Biography

Deivison Pinheiro Franco, University of Amazônia.

Master in Technological Innovation, Specialist in Computer Networks, in Computer Networks Support and in Forensic Sciences (Emphasis in Forensic Computing) and Graduated in Data Processing. Security Analyst of Bank of Amazônia. Professor at various colleges and universities of disciplines like: Computer Forensics, Information Security, Systems Audit, Computer Networks, Computer Architecture and Operating Systems. Computer Forensic Expert, IT Auditor and Pentester with the following certifications: CEH – Certified Ethical Hacker, CHFI – Certified Hacking Forensic Investigator, DSEH – Data Security Ethical Hacker, DSFE – Data Security Forensics Examiner, DSO – Data Security Officer and ISO/IEC 27002 Foundation.

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Published

2013-07-31 — Updated on 2022-04-17

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How to Cite

Franco, D. P., Barboza, F. D., & Cardoso, N. M. (2022). A Secure Method for Authenticity Verification of Handwritten Signatures Through Digital Image Processing and Artificial Neural Networks. International Journal of Communication Networks and Information Security (IJCNIS), 5(2). https://doi.org/10.17762/ijcnis.v5i2.382 (Original work published July 31, 2013)

Issue

Vol. 5 No. 2 (2013)

Section

Research Articles