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  • Multi-script versus single-script scenarios in automatic off-line signature verification

    Author(s)
    Das, Abhijit
    Ferrer, Miguel
    Pal, Umapada
    Pal, Srikanta
    Diaz, Moises
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Pal, Srikanta
    Das, Abhijit
    Year published
    2016
    Metadata
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    Abstract
    This study introduces a novel method to build up a multi-script off-line signature database aggregating many single-script off-line databases, along with a statistical performance analysis method for a fair comparison between single- and multi-script scenarios. This analysis method is based on merging the single-script databases without increasing the number of users (signers) and selecting the users for merging, based on the probability density functions of the users' equal error rates. As similar results are achieved when merging single- and multi-script databases, it is concluded that multi-script signature verification ...
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    This study introduces a novel method to build up a multi-script off-line signature database aggregating many single-script off-line databases, along with a statistical performance analysis method for a fair comparison between single- and multi-script scenarios. This analysis method is based on merging the single-script databases without increasing the number of users (signers) and selecting the users for merging, based on the probability density functions of the users' equal error rates. As similar results are achieved when merging single- and multi-script databases, it is concluded that multi-script signature verification is actually a generalisation and interoperability problem. The study also concludes that a statistical performance analysis method that is Bhattacharyya distance could be used for analysing multi-script versus single-script signature verification scenarios. These results have been obtained after experimenting with nine public databases with five different scripts.
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    Journal Title
    IET Biometrics
    Volume
    5
    Issue
    4
    DOI
    https://doi.org/10.1049/iet-bmt.2016.0010
    Subject
    Biomechanical Engineering
    Artificial Intelligence and Image Processing
    Interdisciplinary Engineering
    Publication URI
    http://hdl.handle.net/10072/101196
    Collection
    • Journal articles

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