Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

TL;DR

This paper introduces a novel, efficient method for online handwritten signature classification and verification using Information Theory quantifiers, outperforming existing techniques in accuracy and computational simplicity.

Contribution

The study presents a new approach utilizing Shannon Entropy, Statistical Complexity, and Fisher Information over Bandt and Pompe symbolization for signature analysis, with a simple classifier.

Findings

Outperforms state-of-the-art methods in accuracy.

Features are computationally efficient and easy to compute.

Effective in classifying signatures into meaningful groups.

Abstract

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.