# Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1705.05787 · HTML: https://mlsanity.com/paper/1705.05787/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 20 of 20)

- [MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition](https://mlsanity.com/paper/2104.01876/) (2021)
- [Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess](https://mlsanity.com/paper/2208.01366/) (2022)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification](https://mlsanity.com/paper/2210.08836/) (2022)
- [Evaluating Synthetic Pre-Training for Handwriting Processing Tasks](https://mlsanity.com/paper/2304.01842/) (2023)
- [Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis](https://mlsanity.com/paper/2303.08811/) (2023)
- [Brain inspired neuronal silencing mechanism to enable reliable sequence identification](https://mlsanity.com/paper/2203.13028/) (2022)
- [Multiscale Feature Learning Using Co-Tuplet Loss for Offline Handwritten Signature Verification](https://mlsanity.com/paper/2308.00428/) (2023)
- [Offline Handwritten Signature Verification Using a Stream-Based Approach](https://mlsanity.com/paper/2411.06510/) (2024)
- [Signature Verification using Geometrical Features and Artificial Neural Network Classifier](https://mlsanity.com/paper/2108.02029/) (2021)
- [Writer Recognition Using Off-line Handwritten Single Block Characters](https://mlsanity.com/paper/2201.10665/) (2022)
- [Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder](https://mlsanity.com/paper/2409.19754/) (2024)
- [Global Regular Network for Writer Identification](https://mlsanity.com/paper/2201.05951/) (2022)
- [Revealing Reliable Signatures by Learning Top-Rank Pairs](https://mlsanity.com/paper/2203.09927/) (2022)
- [Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification](https://mlsanity.com/paper/2308.01136/) (2023)
- [Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification](https://mlsanity.com/paper/2312.00987/) (2023)
- [Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations](https://mlsanity.com/paper/2401.03085/) (2024)
- [Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach](https://mlsanity.com/paper/2401.09467/) (2024)
- [Explainable offline automatic signature verifier to support forensic handwriting examiners](https://mlsanity.com/paper/2405.12695/) (2024)
- [Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures](https://mlsanity.com/paper/2405.14409/) (2024)

## Abstract (arXiv)

Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks. In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user. Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to 6.97% in the literature. We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset.