Online Signature Verification Based on Feature Combination and Classifier Fusion

Abstract

Fusion of multiple classifiers is an area of biometrics research that has recently gained in popularity. A scheme for online signature verification using feature combination and classifier fusion based on F-Tablet was proposed. On one hand, we paid attention to the feature combination, role of writing forces, and suitability of feature sets on each kind of classifier. Performance evaluation on F-Tablet for different classifiers and feature combinations suggested the average performance of most classifiers reached the best results when full coordinate and writing forces were presented. The DTW and SVM outperformed other classifiers in terms of EER with 2.86% and 3.68% respectively. However, when coordinate or writing forces only was offered, the writing forces outperformed trajectory, which indicated a huge information loss, especially in mobile environment that could not capture the writing forces for many sensors. On the other hand, in order to design high performance online signature verification system, it was necessary to consider the fusion of classifiers. The various classifier fusion schemes were compared experimentally. The performance of the fusion system was significantly improved compared with the performance of the single classifiers, with the best EER reaching 1.04%.