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  • 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)
    Muthukkumarasamy, Vallipuram
    Year published
    2007
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    Abstract
    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
    DOI
    https://doi.org/10.1109/MCI.2007.353417
    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
    Publication URI
    http://hdl.handle.net/10072/17901
    Collection
    • Journal articles

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