Off-Line Signature Recognition Based On Angle Features and GRNN Neural Networks

Authors: Laila Y. Fannas, Ahmed Y. Ben Sasi

Abstract:

This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.

Keywords: Signature Recognition, Artificial Neural Network, Angle Features.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1335782

References:

[1] V. K. Madasu and B. C. Lovell, “An Automatic Offline Signature Verification and Forgery Detection System”, IGI Global, 2008,pp. 63- 94.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
[2] O. Elrajubi, “Off-line Signature Verification Based on Fuzzy Logic ”, The Academy of Graduate Studies-Tripoli, Libya, June 2009.
[3] H. Demuth, M. Beale, “Neural Network Toolbox”, Version 4, September 2000.
[4] S. A. Hannan, R. R. Manza, R. J. Ramteke, “Generalized Recognition Neural Network and Radial Basis function for Heart Disease Diagnosis”, International Journal of Computer Applications (0975- 8887), Volume 7-No.13, October 2010.
[5] D. C. Silverman, “A General Regression Artificial Neural Network”, IEEE Transactions on Neural Networks, 2, p. 568, 1991.