Combined Off-Line Signature Verification Using Neural Networks

In this paper, combined off-line signature verification using Neural Network (CSVNN) is presented. The global and grid features are combined to generate new set of features for the verification of signature. The Neural Network (NN) is used as a classifier for the authentication of a signature. The performance analysis is verified on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm.

[1]  Hamid Reza Pourreza,et al.  Offline Signature Verification Using Local Radon Transform and Support Vector Machines , 2009 .

[2]  Phalguni Gupta,et al.  Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory , 2010, ArXiv.

[3]  Javed Ahmed Mahar,et al.  Off-Line Signature Verification of Bank Cheque Having Different Background Colors , 2007, 2007 IEEE/ACS International Conference on Computer Systems and Applications.

[4]  V.N. Sastry,et al.  Multi Objective Portfolio Optimization Models and Its Solution Using Genetic Algorithms , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[5]  Luiz Eduardo Soares de Oliveira,et al.  Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers , 2010, Pattern Recognit..

[6]  M.V. Karki,et al.  Off-Line Signature Recognition and Verification Using Neural Network , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[7]  Hairong Lv,et al.  Off-line signature verification based on deformable grid partition and Hidden Markov Models , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[8]  Tülay Yildirim,et al.  Conic Section Function Neural Network Circuitry for Offline Signature Recognition , 2010, IEEE Transactions on Neural Networks.