Offline Signature Verification Using Deep learning and Genetic Algorithm

Abstract

The process of verifying signatures has wide-ranging applications in computer systems, including financial operations,
electronic document signing, and user identity verification. This approach has the advantage of community acceptance and
presents a less intrusive alternative than other biological authentication methods. Deep learning (DL) and Convolutional Neural
Networks (CNNs) have emerged as prominent tools in the field of signature verification, significantly enhancing the accuracy and effectiveness of these systems by effectively extracting discriminative features from signature images. However, optimizing the hyperparameters in CNN models remains a challenging task, as it directly affects the efficiency and accuracy of the models.
Currently, the design of CNN architectures relies heavily on manual adjustments, which can be time-consuming and may
not yield optimal results. To address this issue, the proposed method focuses on employing a genetic algorithm to evolve
a population of CNN models, thereby enabling the automatic discovery of the most suitable architecture for offline signature
verification. By leveraging the optimization capabilities of the genetic algorithm, the proposed approach aims to improve the
overall performance and effectiveness of the signature verification model. The effectiveness of the proposed method was evaluated using multiple datasets, including BHSig260-Bengali, BHSig260-Hindiin, GPDS, and CEDAR. Through rigorous testing, the approach achieved remarkable discrimination rates with a False Rejection Rate (FRR) of 2.5%, a False Acceptance Rate (FAR) of 3.2%, an Equal Error Rate (EER) of 2.35%, and an accuracy rate of 97.73%.