Multiscale Feature Learning Using Co-Tuplet Loss for Offline Handwritten Signature Verification

TL;DR

This paper introduces MS-SigNet, a multiscale feature learning network with a novel co-tuplet loss for improved offline handwritten signature verification, effectively handling intra- and inter-writer variations.

Contribution

The paper proposes MS-SigNet with co-tuplet loss, a new metric learning approach that captures global and local signature features across multiple scales for better verification.

Findings

Outperforms state-of-the-art methods on four benchmark datasets.

Effectively distinguishes genuine signatures from skilled forgeries.

Handles intra-writer variations and inter-writer similarities robustly.

Abstract

Handwritten signature verification, crucial for legal and financial institutions, faces challenges including inter-writer similarity, intra-writer variations, and limited signature samples. To address these, we introduce the MultiScale Signature feature learning Network (MS-SigNet) with the co-tuplet loss, a novel metric learning loss designed for offline handwritten signature verification. MS-SigNet learns both global and regional signature features from multiple spatial scales, enhancing feature discrimination. This approach effectively distinguishes genuine signatures from skilled forgeries by capturing overall strokes and detailed local differences. The co-tuplet loss, focusing on multiple positive and negative examples, overcomes the limitations of typical metric learning losses by addressing inter-writer similarity and intra-writer variations and emphasizing informative examples. The code is available at https://github.com/ashleyfhh/MS-SigNet. We also present HanSig, a large-scale Chinese signature dataset to support robust system development for this language. The dataset is accessible at https://github.com/hsinmin/HanSig. Experimental results on four benchmark datasets in different languages demonstrate the promising performance of our method in comparison to state-of-the-art approaches.