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Identity authentication using improved on-line signature verification method
Authors: Alisher Kholmatov and Berrin Yanikoglu
Published in: Pattern Recognition Letters
Publication year: 2004
Abstract: We present a system for on-line handwritten signature verification, approaching the
problem as a two-class pattern recognition problem. During enrollment, eight reference
signatures are taken from each subject and statistics describing the variation
in the user’s signatures are extracted from the cross alignment of these. A test signature’s
authenticity is established by first aligning it with each reference signature
for the claimed user. Then, using the alignment scores normalized by profile statistics
as features, the test signature is classified into one of the two classes (genuine
or forgery), using standard pattern classification techniques. We experimented with
the Bayes classifier, Support Vector Machines, and a linear classifier used in conjunction
with the Principal Component Analysis. The linear classifier resulted in a
1.4% error rate for a data set of 94 people and 619 test signatures (genuine signatures
and skilled forgeries). Our method also received the first place at the First
International Signature Verification Competition (SVC2004, 2004).
 

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