# Online Signature Verification based on the Lagrange formulation with 2D and 3D robotic models

arXiv: https://arxiv.org/abs/2503.13573 · HTML: https://mlsanity.com/paper/2503.13573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (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)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [ICDAR 2021 Competition on On-Line Signature Verification](https://mlsanity.com/paper/2106.00739/) (2021)
- [Static and Dynamic Synthesis of Bengali and Devanagari Signatures](https://mlsanity.com/paper/2401.17026/) (2024)
- [Neural network modelling of kinematic and dynamic features for signature verification](https://mlsanity.com/paper/2411.17506/) (2024)
- [Uniform vs. Lognormal Kinematics in Robots: Perceptual Preferences for Robotic Movements](https://mlsanity.com/paper/2405.19081/) (2024)
- [Anthropomorphic Features for On-Line Signatures](https://mlsanity.com/paper/2501.09048/) (2025)

## Abstract (arXiv)

Online Signature Verification commonly relies on function-based features, such as time-sampled horizontal and vertical coordinates, as well as the pressure exerted by the writer, obtained through a digitizer. Although inferring additional information about the writers arm pose, kinematics, and dynamics based on digitizer data can be useful, it constitutes a challenge. In this paper, we tackle this challenge by proposing a new set of features based on the dynamics of online signatures. These new features are inferred through a Lagrangian formulation, obtaining the sequences of generalized coordinates and torques for 2D and 3D robotic arm models. By combining kinematic and dynamic robotic features, our results demonstrate their significant effectiveness for online automatic signature verification and achieving state-of-the-art results when integrated into deep learning models.