A Directional Feature with Energy based Offline Signature Verification Network

Abstract

Signature used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. So, it is very important to have a computerized signature verification system. In an offline signature verification system, dynamic features are not available obviously, but one can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. The author proposes an intelligent network that used directional features and energy density both as inputs to the same network and classifies the signature. A neural network is used as a classifier for this system. The results are compared with both the very basic energy density method and a simple directional feature method of the offline signature verification system and this proposed new network is found very effective as compared to the above two methods, especially for less number of training samples, which can be implemented practically.