Off-line Signature Verification Using an Enhanced Modified Direction Feature with Single and Multi-classifier Approaches

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Author(s)
Armand, Stephane
Blumenstein, Michael
Muthukkumarasamy, Vallipuram
Griffith University Author(s)
Year published
2007
Metadata
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The principal objective of this paper was to investigate the efficiency of the enhanced version of the MDF feature extractor for signature verification. Investigations adding new feature values to MDF were performed, assessing the impact on the verification rate of the signatures, using six-fold cross validation. Two different neural classifiers were used and two methodologies for verification were applied. The experiments conducted, whereby MDF was merged with the new features, provided very encouraging resultsThe principal objective of this paper was to investigate the efficiency of the enhanced version of the MDF feature extractor for signature verification. Investigations adding new feature values to MDF were performed, assessing the impact on the verification rate of the signatures, using six-fold cross validation. Two different neural classifiers were used and two methodologies for verification were applied. The experiments conducted, whereby MDF was merged with the new features, provided very encouraging results
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Journal Title
IEEE Computational Intelligence Magazine
Volume
2
Issue
2
Copyright Statement
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subject
Electrical and Electronic Engineering