# Online Handwritten Signature Verification Based on Temporal-Spatial Graph Attention Transformer

arXiv: https://arxiv.org/abs/2510.19321 · HTML: https://mlsanity.com/paper/2510.19321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 75 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [Multi-Graph Transformer for Free-Hand Sketch Recognition](https://mlsanity.com/paper/1912.11258/) (2019)
- [Embedding and learning with signatures](https://mlsanity.com/paper/1911.13211/) (2019)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [Offline Handwritten Signature Verification - Literature Review](https://mlsanity.com/paper/1507.07909/) (2015)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification](https://mlsanity.com/paper/1901.03398/) (2019)
- [Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature](https://mlsanity.com/paper/1705.06849/) (2017)
- [SM-DTW: Stability Modulated Dynamic Time Warping for signature verification](https://mlsanity.com/paper/2405.11978/) (2024)
- [Neural network modelling of kinematic and dynamic features for signature verification](https://mlsanity.com/paper/2411.17506/) (2024)
- [The Language of Biometrics: Analysing Public Perceptions](https://mlsanity.com/paper/1905.04615/) (2019)
- [Multiscale Feature Learning Using Co-Tuplet Loss for Offline Handwritten Signature Verification](https://mlsanity.com/paper/2308.00428/) (2023)
- [Online Signature Verification using Deep Representation: A new Descriptor](https://mlsanity.com/paper/1806.09986/) (2018)
- [Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks](https://mlsanity.com/paper/1810.07491/) (2018)
- [Deep Learning Methods for Signature Verification](https://mlsanity.com/paper/1912.05435/) (2019)
- [Biometric Signature Verification Using Recurrent Neural Networks](https://mlsanity.com/paper/2205.02934/) (2022)

## Abstract (arXiv)

Handwritten signature verification is a crucial aspect of identity authentication, with applications in various domains such as finance and e-commerce. However, achieving high accuracy in signature verification remains challenging due to intra-user variability and the risk of forgery. This paper introduces a novel approach for dynamic signature verification: the Temporal-Spatial Graph Attention Transformer (TS-GATR). TS-GATR combines the Graph Attention Network (GAT) and the Gated Recurrent Unit (GRU) to model both spatial and temporal dependencies in signature data. TS-GATR enhances verification performance by representing signatures as graphs, where each node captures dynamic features (e.g. position, velocity, pressure), and by using attention mechanisms to model their complex relationships. The proposed method further employs a Dual-Graph Attention Transformer (DGATR) module, which utilizes k-step and k-nearest neighbor adjacency graphs to model local and global spatial features, respectively. To capture long-term temporal dependencies, the model integrates GRU, thereby enhancing its ability to learn dynamic features during signature verification. Comprehensive experiments conducted on benchmark datasets such as MSDS and DeepSignDB show that TS-GATR surpasses current state-of-the-art approaches, consistently achieving lower Equal Error Rates (EER) across various scenarios.