Off-line signature verification using multiple neural network classification structures

Abstract

This paper summarizes a research effort for an off-line signature recognition and verification system. The system uses three kinds of features extracted from the digital image of the signature: global features, grid information features and texture features. For each of them a special one-class-one-network classification structure has been implemented. In order for the system to come to a decision, it uses the results from all the three neural network structures, combined with a simple Euclidean norm.