Signature Recognition Using Conjugate Gradient Neural Networks
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.
[1] Sabourin R, Drouhard J P, 1992, Offline signature verification using
directional PDF and neural networks, Proceedings 11th international
conference on pattern recognition, 2:321-325.
[2] Qi Y Y, Hunt B R, 1994, Signature verification using global and grid
features, Pattern Recognition, 22(12): 1621-1629.
[3] Bajaj R, Chaudhury S, 1997, Signature verification using multiple neural
classifiers, Pattern Recognition, 30(1):1-7.
[4] Sansone C, Vento M, 2000, Signature verification: increasing
performance by a multistage system, Pattern Analysis & Application,
3:169- 181.
[5] Baltzkis H, Papamarkos N, 2001, A new signature verification
technique based on a two stage neural network classifier,
Engineering.
[6] Eric W Brown, [email protected] Applying Neural Networks to
Character Recognition.
[7] Bazzi, Issam, Richard Schwartz and John Makhoul (1999) An omnifont
open-vocabulary OCR system for English and Arabic. Pattern Analysis
and Machine Intelligence 21 495-504.
[8] D. A. Mighell, "Backpropagation and its application to handwritten
signature verication", Advances in Neural Information Processing
Systems I, pp. 340-347, 1989.
[9] Holger Schwenk, and Yoshua Bengio, Adaptive Boosting of Neural
Networks for Character Recognition.
[1] Sabourin R, Drouhard J P, 1992, Offline signature verification using
directional PDF and neural networks, Proceedings 11th international
conference on pattern recognition, 2:321-325.
[2] Qi Y Y, Hunt B R, 1994, Signature verification using global and grid
features, Pattern Recognition, 22(12): 1621-1629.
[3] Bajaj R, Chaudhury S, 1997, Signature verification using multiple neural
classifiers, Pattern Recognition, 30(1):1-7.
[4] Sansone C, Vento M, 2000, Signature verification: increasing
performance by a multistage system, Pattern Analysis & Application,
3:169- 181.
[5] Baltzkis H, Papamarkos N, 2001, A new signature verification
technique based on a two stage neural network classifier,
Engineering.
[6] Eric W Brown, [email protected] Applying Neural Networks to
Character Recognition.
[7] Bazzi, Issam, Richard Schwartz and John Makhoul (1999) An omnifont
open-vocabulary OCR system for English and Arabic. Pattern Analysis
and Machine Intelligence 21 495-504.
[8] D. A. Mighell, "Backpropagation and its application to handwritten
signature verication", Advances in Neural Information Processing
Systems I, pp. 340-347, 1989.
[9] Holger Schwenk, and Yoshua Bengio, Adaptive Boosting of Neural
Networks for Character Recognition.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49908", author = "Jamal Fathi Abu Hasna", title = "Signature Recognition Using Conjugate Gradient Neural Networks", 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.", keywords = "Signature Verification, MATLAB Software,Conjugate Gradient, Segmentation, Skilled Forgery, and Genuine.", volume = "2", number = "8", pages = "1577-5", }