Fathi, Jamal. <br/><br/>
Signature Recognition Using Conjugate Gradient Neural Networks. 
Jamal Fathi, Abu Hasna. 
 - Çanakkale :  World Acad Sci. Eng & Tech-Waset,  2006. 
<br/><br/> 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.<br/> 
<br/><br/><label>ISSN: </label>1307-6884  
<br/><br/><label>Nat. Bib. Agency Control No.: </label>000259632400034  
<br/><br/><label>Subjects--Topical Terms: </label><br/>Near East University Article<br/>Computer science<br/>Yakın Doğu Üniversitesi Makale<br/>Signature Verification<br/>MATLAB Software
<br/><br/><label>LC Class. No.: </label>QA76