@article{(Open Science Index):https://publications.waset.org/pdf/765,
	  title     = {Signature Recognition Using Conjugate Gradient Neural Networks},
	  author    = {Jamal Fathi Abu Hasna},
	  country	= {},
	  institution	= {},
	  abstract     = {There are two common methodologies to verify
signatures: the functional approach and the parametric approach. This
paper presents a new approach for dynamic handwritten signature
verification (HSV) using the Neural Network with verification by the
Conjugate Gradient Neural Network (NN). It is yet another avenue in
the approach to HSV that is found to produce excellent results when
compared with other methods of dynamic. Experimental results show
the system is insensitive to the order of base-classifiers and gets a
high verification ratio.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {2},
	  number    = {8},
	  year      = {2008},
	  pages     = {1698 - 1702},
	  ee        = {https://publications.waset.org/pdf/765},
	  url   	= {https://publications.waset.org/vol/20},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 20, 2008},