Local Feature Based Off-line Signature Verification using Neural Network Classifiers

Signature recognition is probably the oldest biometrical identification method with a high legal acceptance. Although automated signature verification has been studied for more than 30 years this field still lacks the necessary formalization to evaluate and compare different signature verification systems. Our research aims separating the steps of signature verification and dissecting the monolith verification systems into smaller benchmarkable parts. This paper focuses on classification, the last phase of signature verification. In contrast with typical applications, our solution is able to take both global and local features of the signatures into consideration. This also introduces some questions which are all addressed in the paper. Several local features are introduced, and evaluated by using a neural network classifier, with a special emphasis on the usability of shape descriptors.