# Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder

arXiv: https://arxiv.org/abs/2409.19754 · HTML: https://mlsanity.com/paper/2409.19754/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 29 Sep 2024.

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

- [Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.05787/) (2017)
- [Offline Handwritten Signature Verification - Literature Review](https://mlsanity.com/paper/1507.07909/) (2015)
- [Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.00974/) (2016)
- [A comprehensive study of sparse representation techniques for offline signature verification](https://mlsanity.com/paper/1807.05039/) (2018)
- [Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach](https://mlsanity.com/paper/1903.06249/) (2019)

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

- [A Prototypical Signature Approach for Writer-Independent Offline Signature Verification](https://mlsanity.com/paper/2606.18528/) (2026)

## Abstract (arXiv)

Offline handwritten signature verification systems are used to verify the identity of individuals, through recognizing their handwritten signature image as genuine signatures or forgeries. The main tasks of signature verification systems include extracting features from signature images and training a classifier for classification. The challenges of these tasks are twofold. First, genuine signatures and skilled forgeries are highly similar in their appearances, resulting in a small inter-class distance. Second, the instances of skilled forgeries are often unavailable, when signature verification models are being trained. To tackle these problems, this paper proposes a new signature verification method. It is the first model that employs a variational autoencoder (VAE) to extract features directly from signature images. To make the features more discriminative, it improves the traditional VAEs by introducing a new loss function for feature disentangling. In addition, it relies on SVM (Support Vector Machine) for classification according to the extracted features. Extensive experiments are conducted on two public datasets: MCYT-75 and GPDS-synthetic where the proposed method significantly outperformed 13 representative offline signature verification methods. The achieved improvement in distinctive datasets indicates the robustness and great potential of the developed system in real application.