Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
This work addresses biometric authentication for security applications, but it is incremental as it combines existing structural and statistical methods.
The paper tackled offline signature verification by combining graph edit distance with triplet networks, achieving significant performance improvements on MCYT and GPDS benchmark datasets.
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.