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

arXiv: https://arxiv.org/abs/2308.00428 · HTML: https://mlsanity.com/paper/2308.00428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 56 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [Shallow Feature Matters for Weakly Supervised Object Localization](https://mlsanity.com/paper/2108.00873/) (2021)
- [SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/1707.02131/) (2017)
- [Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.05787/) (2017)
- [SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/2201.10138/) (2022)
- [UTSig: A Persian Offline Signature Dataset](https://mlsanity.com/paper/1603.03235/) (2016)
- [Fixed-sized representation learning from Offline Handwritten Signatures of different sizes](https://mlsanity.com/paper/1804.00448/) (2018)

## Built on by (most cited first, 2 of 2)

- [Online Handwritten Signature Verification Based on Temporal-Spatial Graph Attention Transformer](https://mlsanity.com/paper/2510.19321/) (2025)
- [DetailSemNet: Elevating Signature Verification through Detail-Semantic Integration](https://mlsanity.com/paper/2511.16364/) (2025)

## Abstract (arXiv)

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.