UDC 004.93; UDC 004.896
REVIEW AND COMPARISON OF SELECTED METHODS FOR SIGNATURE AUTHENTICATION BASED ON DYNAMIC PARAMETERS
A. H. Tancerov, Postgraduate Student, Department of Programming, PSTU, Penza, Russia;
orcid.org/0009-0006-7695-0514, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
E. A. Danilov, PhD (in technical sciences), Associate Professor, Department of Programming, PSTU, Penza, Russia;
orcid.org/0000-0003-4114-7036, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
A. I. Martyshkin, PhD (in technical sciences), Associate Professor, Head of Department of Programming, PSTU, Penza, Russia;
orcid.org/0000-0002-3358-4394, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
The article highlights trends in the development of dynamic signature authentication systems market, which is projected to experience significant growth with a 21.5 % CAGR from 2023 to 2028. The relevance of this research is supported by the increasing number of patents and scientific publications in the field. The paper presents a comprehensive classification of features used in signature authentication procedure, sys tematized by categories. The aim of the work is to conduct a comprehensive review and critical analysis of existing parametric and functional methods for dynamic signature authentication. A comparative evaluation of effectiveness and computational costs of the methods under consideration is carried out, their strengths and weaknesses are identified, and practical application scenarios for verification systems are justified. In addition, the study examines prospects for integrating machine learning and artificial intelligence techniques to enhance reliability and security of signature authentication processes, and emphasizes the need to address information security issues and further optimize data processing algorithms in light of the growing number of patent applications and scientific publications in this field.
Key words: : signature authentication, dynamic signature, features, parametric methods, functional meth ods, support vector machine, neural networks.