DocumentCode :
2510349
Title :
On-Line Signature Verification Using 1-D Velocity-Based Directional Analysis
Author :
Ibrahim, Muhammad Talal ; Kyan, Matthew ; Khan, M. Aurangzeb ; Guan, Ling
Author_Institution :
Ryerson Multimedia Res. Lab., Ryerson Univ., Toronto, ON, Canada
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
3830
Lastpage :
3833
Abstract :
In this paper, we propose a novel approach for identity verification based on the directional analysis of velocity-based partitions of an on-line signature. First, inter-feature dependencies in a signature are exploited by decomposing the shape (horizontal trajectory, vertical trajectory) into two partitions based on the velocity profile of the base-signature for each signer, which offers the flexibility of analyzing both low and high-curvature portions of the trajectory independently. Further, these velocity-based shape partitions are analyzed directionally on the basis of relative angles. Support Vector Machine (SVM) is then used to find the decision boundary between the genuine and forgery class. Experimental results demonstrate the superiority of our approach in on-line signature verification in comparison with other techniques.
Keywords :
digital signatures; handwriting recognition; support vector machines; 1D velocity-based directional analysis; high-curvature portions; identity verification; interfeature dependencies; low curvature portions; online signature verification; support vector machine; velocity-based shape partitions; Artificial neural networks; Databases; Forgery; Shape; Support vector machines; Training; Trajectory; curvature; inter-feature dependencies; on-line signature; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.933
Filename :
5597554
Link To Document :
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