Enhancing Offline Signature Verification through CNN Model Optimization with PSO Algorithm - Abdulbaset M. Qaid Musleh, Abdoulwase M. Obaid Al-Azzani. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The verification of handwritten signatures is integral to numerous applications such as authentication and document verification. The efficacy of an offline signature verification system relies heavily on the feature extraction stage, because it significantly affects the performance of the system. Both the quality and quantity of extracted features play pivotal roles in enabling the system to distinguish between genuine and forged signatures. In this study, we introduce a novel approach aimed at optimizing the hyperparameters of a Convolutional Neural Network (CNN) model for handwritten signature verification by leveraging a Particle Swarm Optimization (PSO) algorithm. The PSO algorithm, inspired by the flocking behavior of birds, is a population-based optimization method. We delineated a search space encompassing various hyperparameter ranges, including the number of convolutional filters, dense layers, dropout rate, and learning rate. Through iterative updates to the positions and velocities of the particles, the PSO algorithm navigates this search space to identify the optimal set of hyperparameters that maximizes the accuracy of the CNN model. Our approach was evaluated across diverse datasets including BHSig260-Bengali, BHSig260-Hindi, GPDS, and CEDAR, each containing a varied assortment of handwritten signature images. The experimental results demonstrate the effectiveness of our proposed method, achieving a remarkable accuracy of 98.3% on the testing dataset.