Model Compression Based Lightweight Online Signature Verification Framework

Abstract

Advances in networking and digital technologies have led to the widespread usage of Online Signature Verification (OSV) frameworks in real-time settings to validate a user's identity. Because of the superior performance of Deep Learning frameworks, CNN-based models have been widely used for solving difficult computer vision tasks such as Object Detection, Object Segmentation, and so on. The biggest impediment to OSV adoption of CNN-based models is the growing size of CNN models. This prohibits OSV frameworks from being widely used in devices with minimal computational resources, such as mobile/ embedded devices. The newly popular topic of CNN model pruning aims to solve this problem by deleting filters and neurons that do not contribute to the model's learning. Optimal CNN-based OSV frameworks are obtained by removing the less important filters and neurons and fine-tuning the pruned networks. In line with this, we offer a novel light weight OSV architecture in which a detailed ablation research is performed to examine the contribution of each layer, and non-contributive layers are deleted based on the analysis. As a result, ideal low weight models with improved classification accuracies and the ability to be applied in real-time devices emerge. Our model's performance is thoroughly tested on three commonly used datasets: MCYT-100 (DB1), SVC, and SUSIG. In MCYT-100, SVC, and SUSIG datasets, the pruned model achieves a state-of-the-art EER of 7.98%, 3.65%, and 12.39% in the skilled-1 group, respectively. The efficiency of pruning-based OSV frameworks has been demonstrated in experiments.