IMPACT OF AUGMENTATION METHODS IN ONLINE SIGNATURE VERIFICATION
Abstract:
Online signature verification is a prominent method for authenticating individuals in digital transactions and document signing processes. With the increasing reliance on digital platforms and the rise of remote interactions, the need for reliable signature verification systems has become more crucial. To address this demand, researchers have explored various techniques, including data augmentation, to improve the accuracy and robustness of online signature verification systems.
This paper aims to investigate the impact of augmentation methods on online signature verification performance. Augmentation methods involve generating additional training samples by applying various transformations to the original signature data. These transformations may include scaling, rotation, translation, noise injection, and elastic deformations, among others. The augmented data is then used to train machine learning or deep learning models for signature verification.
The study reviews different augmentation techniques proposed in the literature and evaluates their effectiveness in improving signature verification accuracy. It examines the impact of augmentation on various aspects of the verification system, such as robustness against forgeries, intra-user variations, and inter-user variations. The paper also discusses the computational cost associated with augmentation methods and their potential limitations.
Furthermore, the paper explores the challenges and considerations in selecting appropriate augmentation techniques for online signature verification. Factors such as dataset size, signature complexity, and the nature of the application influence the choice of augmentation methods. The study provides insights into the trade-offs involved in selecting augmentation techniques based on the specific requirements of the signature verification system.
The results of this research contribute to the understanding of the significance of augmentation methods in online signature verification. By quantifying the impact of different augmentation techniques on the performance of signature verification systems, this study aids in the development of more accurate and reliable solutions for identity authentication in digital transactions. The findings can guide researchers, practitioners, and system developers in selecting appropriate augmentation strategies for their specific applications.
Keywords: Online signature verification, augmentation methods, data augmentation, signature authentication, machine learning, deep learning.