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Kokubunji, Tokyo, Japan
Oct. 26, 2004 to Oct. 29, 2004
ISBN: 0-7695-2187-8
pp: 161-166
Sargur N. Srihari , State University of New York at Buffalo
Aihua Xu , State University of New York at Buffalo
Meenakshi K. Kalera , State University of New York at Buffalo
ABSTRACT
Learning strategies and classification methods for verification of signatures from scanned documents are proposed and evaluated. Learning strategies considered are writer-independent — those that learn from a set of signature samples (including forgeries) prior to enrollment of a writer, and writer dependent — those that learn only from a newly enrolled individual. Classification methods considered include two distance based methods (one based on a threshold, which is the standard method of signature verification and biometrics, and the other based on a distance probability distribution), a Nave Bayes (NB) classifier based on pairs of feature bit values and a support vector machine (SVM). Two scenarios are considered for the writer-dependent scenario: (i) without forgeries (one-class problem) and (ii) with forgery samples being available (two-class problem). The features used to characterize a signature capture local geometry, stroke and topology information in the form of a binary vector. In the one-class scenario distance methods are superior while in the two-class SVM based method outperforms the other methods.
INDEX TERMS
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CITATION
Sargur N. Srihari, Aihua Xu, Meenakshi K. Kalera, "Learning Strategies and Classification Methods for Off-Line Signature Verification", IWFHR, 2004, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition 2004, pp. 161-166, doi:10.1109/IWFHR.2004.61
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