# Exploring Transformers for On-Line Handwritten Signature Verification

arXiv: https://arxiv.org/abs/2307.01663 · HTML: https://mlsanity.com/paper/2307.01663/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization](https://mlsanity.com/paper/1912.04761/) (2019)
- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition](https://mlsanity.com/paper/2206.01441/) (2022)
- [TypeFormer: Transformers for Mobile Keystroke Biometrics](https://mlsanity.com/paper/2212.13075/) (2022)
- [Exploiting Complexity in Pen- and Touch-based Signature Biometrics](https://mlsanity.com/paper/1905.03676/) (2019)
- [Introduction to Presentation Attacks in Signature Biometrics and Recent Advances](https://mlsanity.com/paper/2302.08320/) (2023)

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

- [GAFSV-Net: A Vision Framework for Online Signature Verification](https://mlsanity.com/paper/2605.00120/) (2026)
- [Online Signature Verification Using Augmented Path Signature and T-Mamba](https://mlsanity.com/paper/2609.08276/) (2026)

## Abstract (arXiv)

The application of mobile biometrics as a user-friendly authentication method has increased in the last years. Recent studies have proposed novel behavioral biometric recognition systems based on Transformers, which currently outperform the state of the art in several application scenarios. On-line handwritten signature verification aims to verify the identity of subjects, based on their biometric signatures acquired using electronic devices such as tablets or smartphones. This paper investigates the suitability of architectures based on recent Transformers for on-line signature verification. In particular, four different configurations are studied, two of them rely on the Vanilla Transformer encoder, and the two others have been successfully applied to the tasks of gait and activity recognition. We evaluate the four proposed configurations according to the experimental protocol proposed in the SVC-onGoing competition. The results obtained in our experiments are promising, and promote the use of Transformers for on-line signature verification.