Off-line Signature Verification: An Approach Based on Combining Distances and One-class Classifiers

This paper presents an off-line signature verification system composed of a combination of several different classifiers. Identity authentication is a very important characteristics specially in systems that requires a high degree of security such as in bank transactions. In our experiments, one-class classifier was used to create a signature verification system, consequently only genuine signatures were necessary for the training phase. We proposed five distances measurement as features for the classification system. The distances extracted from the signature database were: furthest, nearest, template, central and ncentral. Also, a normalization procedure was applied to turn the distance scale invariant. These distances were combined using four operation: product, mean, maximum and minimum. The calculated distances were used as a feature vector to represent the signatures. Finally, the distances measurement and their combinations were used as input vector for different classifiers. The proposed signature verification method obtained very good rates.