# Biometric Signature Verification Using Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2205.02934 · HTML: https://mlsanity.com/paper/2205.02934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [BiosecurID: a multimodal biometric database](https://mlsanity.com/paper/2111.03472/) (2021)

## Built on by (most cited first, 1 of 1)

- [Online Handwritten Signature Verification Based on Temporal-Spatial Graph Attention Transformer](https://mlsanity.com/paper/2510.19321/) (2025)

## Abstract (arXiv)

Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-the-art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeries.