Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
This work addresses signature verification for security applications, offering a simpler and faster approach compared to existing methods, though it appears incremental as it builds on known information theory concepts.
The paper tackled online handwritten signature verification by introducing a method using six information theory features (Shannon Entropy, Statistical Complexity, Fisher Information) for classification with a One-Class SVM, achieving results that surpass state-of-the-art techniques with higher-dimensional features.
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.