Signature Verification Using Conic Section Function Neural Network

Abstract

AbstractThis paper presents a new approach for off-line signature verification based on a hybrid neural network (Conic Section Function Neural Network-CSFNN). Artificial Neural Networks (ANNs) have recently become a very important method for classification and verification problems. In this work, CSFNN was proposed for the signature verification and compared with two well known neural network architectures (Multilayer Perceptron-MLP and Radial Basis Function-RBF Networks). The proposed system was trained and tested on a signature database consisting of a total of 304 signature images taken from 8 different persons. A total of 256 samples (32 samples for each person) for training and 48 fake samples (6 fake samples belonging to each person) for testing were used. The results were presented and the comparisons were also made in terms of FAR (False Acceptance Rate) and FRR (False Rejection Rate).KeywordsHide LayerRadial Basis FunctionDynamic Time WarpingNeural Network ArchitectureFalse Acceptance RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.