Revealing Reliable Signatures by Learning Top-Rank Pairs

TL;DR

This paper introduces a novel method for offline signature verification that learns top-rank pairs to enhance the reliability of signature identification, especially in critical legal and financial contexts.

Contribution

It proposes a new top-rank pair learning scheme that improves the absolute reliability of signature verification, outperforming existing methods on benchmark datasets.

Findings

Achieves higher pos@top on BHSig datasets

Shows improved AUC and accuracy

Enhances reliability in signature verification

Abstract

Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields. In specific scenarios like confirming financial documents and legal instruments, ensuring the absolute reliability of signatures is of top priority. In this work, we proposed a new method to learn "top-rank pairs" for writer-independent offline signature verification tasks. By this scheme, it is possible to maximize the number of absolutely reliable signatures. More precisely, our method to learn top-rank pairs aims at pushing positive samples beyond negative samples, after pairing each of them with a genuine reference signature. In the experiment, BHSig-B and BHSig-H datasets are used for evaluation, on which the proposed model achieves overwhelming better pos@top (the ratio of absolute top positive samples to all of the positive samples) while showing encouraging performance on both Area Under the Curve (AUC) and accuracy.