# A writer-independent approach for offline signature verification using deep convolutional neural networks features

arXiv: https://arxiv.org/abs/1807.10755 · HTML: https://mlsanity.com/paper/1807.10755/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification](https://mlsanity.com/paper/2204.07246/) (2022)
- [Offline Handwritten Signature Verification Using a Stream-Based Approach](https://mlsanity.com/paper/2411.06510/) (2024)
- [Revealing Reliable Signatures by Learning Top-Rank Pairs](https://mlsanity.com/paper/2203.09927/) (2022)
- [Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations](https://mlsanity.com/paper/2401.03085/) (2024)
- [Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems](https://mlsanity.com/paper/2608.09964/) (2026)

## Abstract (arXiv)

The use of features extracted using a deep convolutional neural network (CNN) combined with a writer-dependent (WD) SVM classifier resulted in significant improvement in performance of handwritten signature verification (HSV) when compared to the previous state-of-the-art methods. In this work it is investigated whether the use of these CNN features provide good results in a writer-independent (WI) HSV context, based on the dichotomy transformation combined with the use of an SVM writer-independent classifier. The experiments performed in the Brazilian and GPDS datasets show that (i) the proposed approach outperformed other WI-HSV methods from the literature, (ii) in the global threshold scenario, the proposed approach was able to outperform the writer-dependent method with CNN features in the Brazilian dataset, (iii) in an user threshold scenario, the results are similar to those obtained by the writer-dependent method with CNN features.