OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification

TL;DR

This paper introduces OSVNet, a deep convolutional Siamese network framework for online signature verification that effectively learns intra-personal variations and improves accuracy over existing methods.

Contribution

The paper proposes a novel deep convolutional Siamese network for online signature verification, extending it for one-shot learning and demonstrating superior performance on benchmark datasets.

Findings

Achieves lower error rates than recent state-of-the-art OSV techniques.

Effectively reduces intra-writer variability and enhances inter-writer discrimination.

Demonstrates robustness across multiple benchmark datasets.

Abstract

Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to the large intra-individual variability, there is a critical requirement to accurately learn the intra-personal 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 extracts robust feature descriptions based on metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increases inter-individual variability (Genuine-Forgery) and directs the DCSN for effective discriminative representation learning for online signatures and extend it for one shot learning framework. Comprehensive experimentation conducted on three widely accepted benchmark datasets MCYT-100 (DB1), MCYT-330 (DB2) and SVC-2004-Task2 demonstrate the capability of our framework to distinguish the genuine and forgery samples. Experimental results confirm the efficiency of deep convolutional Siamese network based OSV by achieving a lower error rate as compared to many recent and state-of-the art OSV techniques.