# Neural network modelling of kinematic and dynamic features for signature verification

arXiv: https://arxiv.org/abs/2411.17506 · HTML: https://mlsanity.com/paper/2411.17506/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [ICDAR 2021 Competition on On-Line Signature Verification](https://mlsanity.com/paper/2106.00739/) (2021)
- [Sequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUs](https://mlsanity.com/paper/2101.09461/) (2021)
- [Static and Dynamic Synthesis of Bengali and Devanagari Signatures](https://mlsanity.com/paper/2401.17026/) (2024)

## Built on by (most cited first, 3 of 3)

- [Online Signature Verification based on the Lagrange formulation with 2D and 3D robotic models](https://mlsanity.com/paper/2503.13573/) (2025)
- [Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators](https://mlsanity.com/paper/2509.17381/) (2025)
- [Online Handwritten Signature Verification Based on Temporal-Spatial Graph Attention Transformer](https://mlsanity.com/paper/2510.19321/) (2025)

## Abstract (arXiv)

Online signature parameters, which are based on human characteristics, broaden the applicability of an automatic signature verifier. Although kinematic and dynamic features have previously been suggested, accurately measuring features such as arm and forearm torques remains challenging. We present two approaches for estimating angular velocities, angular positions, and force torques. The first approach involves using a physical UR5e robotic arm to reproduce a signature while capturing those parameters over time. The second method, a cost effective approach, uses a neural network to estimate the same parameters. Our findings demonstrate that a simple neural network model can extract effective parameters for signature verification. Training the neural network with the MCYT300 dataset and cross validating with other databases, namely, BiosecurID, Visual, Blind, OnOffSigDevanagari 75 and OnOffSigBengali 75 confirm the models generalization capability.