A comprehensive study of sparse representation techniques for offline signature verification

TL;DR

This paper introduces a sparse representation-based feature extraction method for offline signature verification, achieving state-of-the-art accuracy across multiple datasets by optimizing parameters and employing advanced pooling, segmentation, and preprocessing techniques.

Contribution

It presents a novel sparse representation approach with optimized parameters, a new pooling function, and a tailored segmentation strategy for improved signature verification performance.

Findings

Achieved state-of-the-art results on multiple signature datasets.

Identified 2nd order statistics as a powerful pooling function.

Demonstrated effectiveness in writer-dependent scenarios with few samples.

Abstract

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.