# A comprehensive study of sparse representation techniques for offline signature verification

arXiv: https://arxiv.org/abs/1807.05039 · HTML: https://mlsanity.com/paper/1807.05039/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

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

- [Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder](https://mlsanity.com/paper/2409.19754/) (2024)
- [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)
- [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)

In this work, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS and UTSIG. Beyond the accuracy improvements, several major parameters associated with sparse representation; such as selected configuration, dictionary size, sparsity level and positivity priors are investigated. Besides, it is evinced that 2nd order statistics of the sparse codes is a powerful pooling function for the formation of the global signature descriptor. Also, a thorough evaluation of the effects of preprocessing is introduced by an automated algorithm in order to select the optimum thinning level. Finally, a segmentation strategy which employs a special form of spatial pyramid tailored to the problem of sparse representation is presented along with the enhancing of the produced descriptor on meaningful areas of the signature as emerged from the BRISK key-point detection mechanism. The obtained state-of-the-art results on the most challenging signature datasets provide a strong indication towards the benefits of learned features, even in writer dependent (WD) scenarios with a unique model for each writer and only a few available reference samples of him/her.