Online Signature Verification by Few-Shot Separable Convolution Based Deep Learning

Online Signature Verification (OSV) is a widely used biometric feature for recognized and authorized technique to authenticate a writers's distinctiveness and behavioral characteristic. Owing to huge intra-individual changeability, OSV is a challenging problem. Usage of online signatures in m-commerce and m-payment demands for light weight frameworks to classify a signature. The recent OSV models grounded on convolutional neural networks (CNN) and its variants are heavy weight and computationally intensive due to higher amount of parameters to learn. In this context, we put forward a CNN centric OSV framework which uses a stack of depthwise separable (DWS) convolution layers, which makes the framework light weight and enables the few shot learning for signature verification with quite higher accuracy compared to conventional deep learning models. To prove the robustness of our proposed framework, we performed exhaustive experimental evaluations with three standard datasets i.e. MCYT-100 (DB1), SUSIG-Visual corpus and SVC-2004-Task2. Experimental results confirm the efficiency of depthwise separable convolutions grounded OSV by realizing a lesser error rate as related to various current and state-of-the art OSV frameworks.