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Title: Signature verification using Local Directional Pattern (LDP)
Authors: Ferrer, Miguel A. 
Vargas, Francisco
Travieso, Carlos M. 
Alonso, Jesús B. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Databases , Training , Forgery , Robustness , Image segmentation , Off-line handwritten signature verification , Pattern recognition , Gray level information , Texture features , Local directional pattern , SVM
Issue Date: 2010
Publisher: 1071-6572
Journal: Proceedings - International Carnahan Conference on Security Technology 
Conference: 44th Annual 2010 IEEE International Carnahan Conference on Security Technology 
44th Annual 2010 IEEE International Carnahan Conference on Security Technology, ICCST 2010 
Abstract: A method for Off-line handwritten signature verification is described in this paper. Recently, several papers have proposed pseudo dynamic methods based on the ink deposition process to discriminate between genuine and fake signatures. The major problem of those methods is the ink texture normalization in order to make the system invariable to the pen. The more extreme pen normalization is the binarization. This paper explores the usefulness of texture based measures with binarized signatures. In particularly, it proposes to apply the gray scale features local binary pattern (LBP) and local directional pattern (LDP) features to characterize black and white static signatures. The experiments done with MCYT75, GPDS300signature and GPDS960signature corpus shown that LDP are very adequate parameters for automatic verification of black and white static signatures. The results are obtained training a Support Vector Machine (SVM) classifier with genuine samples and random forgeries while random and skilled forgeries have been used for testing it.
ISBN: 9781424474004
ISSN: 1071-6572
DOI: 10.1109/CCST.2010.5678680
Source: Proceedings - International Carnahan Conference on Security Technology[ISSN 1071-6572] (5678680), p. 336-340
Appears in Collections:Actas de congresos
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