Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

TL;DR

This paper introduces a hybrid offline signature verification method that combines graph edit distance with deep neural network-based metric learning, achieving improved accuracy by leveraging their complementary strengths.

Contribution

It presents a novel combination of structural graph-based and statistical neural network models for offline signature verification.

Findings

Significant performance improvements on MCYT and GPDS datasets

Complementary benefits of combining structural and statistical models

Enhanced robustness against forgeries

Abstract

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties.