Ramesh, VE and Murty, Narasimha M (1999) Off-line signature verification using genetically optimized weighted features. In: Pattern Recognition, 32 (2). pp. 217-233.
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This paper is concerned with off-line signature verification. Four different types of pattern representation schemes have been implemented, viz., geometric features, moment-based representations, envelope characteristics and tree-structured Wavelet features. The individual feature components in a representation are weighed by their pattern characterization capability using Genetic Algorithms. The conclusions of the four subsystems teach depending on a representation scheme) are combined to form a final decision on the validity of signature. Threshold-based classifiers (including the traditional confidence-interval classifier), neighbourhood classifiers and their combinations were studied. Benefits of using forged signatures for training purposes have been assessed. Experimental results show that combination of the Feature-based classifiers increases verification accuracy. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science.|
|Keywords:||O¤-line signature veriÞcation;Genetic algorithms;Tree-structured wavelets;Threshold-based classiÞers; Neighbourhood classiÞers;Hybrid classiÞer;Combination of classiÞers|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||29 Jun 2011 05:18|
|Last Modified:||29 Jun 2011 05:18|
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