Year: 2025
Type: article
Abstract: In this article, we present an algorithm to determine the authenticity of a signature with high probability. This study addresses the increasing need for robust and accurate methods of verifying offline signatures, which are crucial in various legal and financial contexts. By combining geometric properties of curves and advanced image processing techniques, our approach effectively distinguishes between genuine and forged signatures. The core of our method utilizes multiple knot B-splines in approximation theory, mean curvature analysis, and curve fitting, providing a comprehensive framework for signature verification. Our findings demonstrate a significant improvement in accuracy over existing methods, as validated by several empirical examples. This research not only contributes to the field of signature verification but also opens avenues for future studies to enhance and adapt our algorithm for broader applications in security and authentication systems.
Source: Nonlinear Engineering
Authors: Ebaa Abdulsattar Jaber, Alireza Ataei, Ali Tavakoli
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,187
OpenAlex ID: W4411265173