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Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04)
Learning Strategies and Classification Methods for Off-Line Signature Verification
Kokubunji, Tokyo, Japan
October 26-October 29
ISBN: 0-7695-2187-8
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
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.
Citation:
Sargur N. Srihari, Aihua Xu, Meenakshi K. Kalera, "Learning Strategies and Classification Methods for Off-Line Signature Verification," iwfhr, pp.161-166, Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04), 2004
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