IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 2    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 2,February 2012
Off-line Signature Verification Using Neural Network
Full Text(PDF, )  PP.361-365  
Ashwini Pansare, Shalini Bhatia
Biometrics, Error back propogation algorithm, Geometric features, Horizontal and vertical splitting, Neural netw ork
A number of biometric techniques have been proposed for personal identification in the past. Among the vision-based ones are face rec-ognition, fingerprint recognition, iris scanning and retina scanning. Voice recognition or signature verification are the most widely known among the non-vision based ones.As signatures continue ti play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. A signature by an authorized person is considered to be the "seal of approval" and remains the most preferred means of authentication.The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verifcation. A verification stage includes applying the extracted features of test signature to a trained neural network which will classify it as a genuine or forged.
[1] Bradley Schafer, Serestina Viriry “An Offline Signature Verification system” IEEE International conference on signals and image processing application, 2009.

[2] Ramachandra A. C ,Jyoti shrinivas Rao”Robust Offline signature verification based on global features” IEEE International Advance Computing Conference ,2009.

[3] J Edson, R. Justino, F. Bortolozzi and R. Sabourin, "An off-line signature verification using HMM for Random,Simple and Skilled Forgeries", Sixth International Conference on Document Analysis and Recognition, pp.1031-1034, Sept.2001. 211-222, Dec.2000.

[4] J Edson, R. Justino, A. El Yacoubi, F. Bortolozzi and R. Sabourin, "An off-line Signature Verification System Using HMM and Graphometric features", DAS 2000

[5] R. Plamondon and S.N. Srihari, "Online and Offline Handwriting Recognition: A Comprehensive Survey", IEEE Tran. on Pattern Analysis and Machine Intelligence, vol.22 no.1, pp.63-84, Jan.2000.

[6] Prasad A.G. Amaresh V.M. “An offline signature verification system”

[7] B. Fang, C.H. Leung, Y.Y. Tang, K.W. Tse, P.C.K. Kwok and Y.K. Wong, "Off-line signature verification by the tracking of feature and stroke positions", Pattern Recognition 36, 2003, pp. 91–101.

[8] Martinez, L.E., Travieso, C.M, Alonso, J.B., and Ferrer, M. Parameterization of a forgery Handwritten Signature Verification using SVM. IEEE 38thAnnual 2004 International Carnahan Conference on Security Technology ,2004 PP.193-196

[9] “An Introduction to Artificial Neural Systems” by Jacek M. Zurada, West Publishing Company 1992

Untitled Page