Signature Authentication Verification using Siamese Network

Signature verification plays a pivotal role in ensuring security across various domains, including financial transactions, legal documents, and access control systems. Traditionally, authentication systems have relied on specific pointers or reference points within a signature to confirm its legitimacy. However, this approach has inherent limitations as it assumes the consistent presence of these reference points across different instances of the signature. In this paper, we introduce an approach to signature verification that eliminates the dependence on pointers or reference points. Instead, we propose a machine learning-based technique that leverages the unique characteristics of signature strokes and patterns to establish authenticity. By analyzing features such as stroke pressure, speed and curvature, our system can accurately verify their validity without the need for predefined pointers or reference templates. Our signature verification system employs a sophisticated neural network architecture trained on an extensive dataset comprising genuine and forged signatures. Through rigorous experimentation and validation processes, we demonstrate the system’s robustness and remarkable accuracy in distinguishing authentic signatures from forgeries, even in cases where traditional pointer-based methods may falter. This innovative approach offers unparalleled flexibility and adaptability in accommodating evolving signature styles and variations. By enhancing security and providing a more versatile solution, our approach represents a significant step forward in the field of signature verification, with far-reaching implications for a wide range of industries and applications.