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Application of a steerable wavelet transform using neural network for signature verification

DSpace at IIT Bombay

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Title Application of a steerable wavelet transform using neural network for signature verification
 
Creator FADHEL, EA
BHATTACHARYYA, P
 
Subject recognition
design
neural networks
off-line signature verification
wavelet transform
 
Description This paper describes a technique for the problem of off-line signature verification using a steerable wavelet transform. The signature has been treated as a two-dimensional image and uses the wavelet as a tool of data reduction and feature selection. Feed forward neural network based architecture is used for both training and classification, because of the generalisation, fault tolerance, and such other capabilities of the neural network. Besides, the small length of the wavelet coefficients vector, which is used as a feature vector reduces the complexity of the neural network in terms of the number of neurons and time of training. Experimental results based on 300 signatures from 30 persons are presented, which show that wavelet has great potential for off-line signature verification.
 
Publisher SPRINGER VERLAG
 
Date 2011-08-30T07:49:25Z
2011-12-26T12:58:50Z
2011-12-27T05:49:27Z
2011-08-30T07:49:25Z
2011-12-26T12:58:50Z
2011-12-27T05:49:27Z
1999
 
Type Article
 
Identifier PATTERN ANALYSIS AND APPLICATIONS, 2(2), 184-195
1433-7541
http://dx.doi.org/10.1007/s100440050027
http://dspace.library.iitb.ac.in/xmlui/handle/10054/12242
http://hdl.handle.net/10054/12242
 
Language en