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Usually, in a real-world scenario, few signature samples are available totrain an automatic signature verification system (ASVS). However, such systemsdo indeed need a lot of signatures to achieve an acceptable performance.Neuromotor signature duplication methods and feature space augmentation methodsmay be used to meet the need for an increase in the number of samples. Suchtechniques manually or empirically define a set of parameters to introduce adegree of writer variability. Therefore, in the present study, a method toautomatically model the most common writer variability traits is proposed. Themethod is used to generate offline signatures in the image and the featurespace and train an ASVS. We also introduce an alternative approach to evaluatethe quality of samples considering their feature vectors. We evaluated theperformance of an ASVS with the generated samples using three well-knownoffline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVMclassifier was trained using one genuine signature per writer and theduplicates generated in the image space, the Equal Error Rate (EER) decreasedfrom 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04%using the feature space augmentation technique. We also verified that the modelthat generates duplicates in the image space reproduces the most common writervariability traits in the three different datasets.

Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation