Online Signature Classification based on Dynamic Nature of Features Selection Framework

Abstract

In recent digital age, online signature verification plays a key role in authentication including security standards across many industries, such as financial, legal, and ecommerce. The World Bank’s data shows the global digital economy is growing fast, with internet usage nearly 60% of people worldwide. According to numbers from the International Telecommunications Union, over 4.7 billion individuals have become internet users with so many user doing internet’s online, security and trust for online transactions are important issues. Forensics and biometrics are emerging as key players in this area. Verifying signatures digitally is one important use. As in the study mentioned earlier, using machine learning can help make signature verification systems more accurate and reliable. Our study describes an online verification method using machine learning that is based on the dynamic features of a signature and compares the outcomes to methods already in use. The online signature verification has been validated using supervised learning (K-nearest neighbour (KNN)). This research aimed to enhance authenticity and reduce the occurrence of false positives as its primary objectives. The outcomes show that this methodology has better authenticity than the current methods. The Signature Verification System (SVS) 2004 based signature data-sets is utilized in the tests.