OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to large intraindividual variability, there is a critical requirement to accurately learn the intrapersonal variations of the signature to achieve higher classification accuracy. To achieve this, in this paper, we propose an OSV framework based on deep convolutional Siamese network (DCSN). DCSN automatically extract robust feature descriptions based on metricbased loss function which decreases intrawriter variability (GenuineGenuine) and increase interindividual variability (GenuineForgery) and guides the DCSN for effective discriminative representation learning for online signatures. Experiments conducted on three widely accepted datasets MCYT100 (DB1), MCYT330 (DB2) and SVC2004Task2 emphasize the capability of our framework to distinguish the genuine and forgery samples. Experimental results confirm the efficiency of the proposed DCSN in one shot learning by achieving a lower error rate as compared to many recent and stateofthe art OSV models.

Online Signature Verification by FewShot Separable Convolution Based Deep Learning Online Signature Verification (OSV) is a widely used biometric feature for recognized and authorized technique to authenticate a writersu0027s distinctiveness and behavioral characteristic. Owing to huge intraindividual changeability, OSV is a challenging problem. Usage of online signatures in mcommerce and mpayment 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. MCYT100 (DB1), SUSIGVisual corpus and SVC2004Task2. Experimental results confirm the efficiency of depthwise separable convolutions grounded OSV by realizing a lesser error rate as related to various current and stateofthe art OSV frameworks.