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 intra-individual 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 metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increase inter-individual variability (Genuine-Forgery) and guides the DCSN for effective discriminative representation learning for online signatures. Experiments conducted on three widely accepted datasets MCYT-100 (DB1), MCYT-330 (DB2) and SVC-2004-Task2 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 state-of-the art OSV models.