Learning Strategies and Classification Methods for Off-Line Signature Verification

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 sample (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.