On the Power of Feature Analyzer for Signature Verification

Abstract

This paper is concerned with verification of signatures using feature analysis and non linear classifier. Signatures are collected and scanned to obtain input image. Preprocessing involves removal of noise and making the input image size invariant. Feature analyzer can reduce the large domain of feature space and extract invariable information. Because of non linearity present in the input, a non linear classifier is used. Instead of using feed forward neural network, multiple feed forward neural networks are used which are trained in the form of ensemble. Using such ensemble makes the system more general than a regular single neural network based system. Resilient back propagation algorithm has been used for each neural network training to achieve faster recognition. Significant amount of training and testing has been performed using 10 fold cross validation and resultant impressive recognition accuracy (More than 90%) proves the effectiveness of the system.