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  • Handwritten Signature Verification Using Complementary Statistical Models

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    80464_1.pdf (437.9Kb)
    Author(s)
    McCabe, A
    Trevathan, J
    Griffith University Author(s)
    Trevathan, Jarrod
    Year published
    2009
    Metadata
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    Abstract
    This paper describes a system for performing handwritten signature verification using complementary statistical models. The system analyses both the static features of a signature (e.g., shape, slant, size), and its dynamic features (e.g., velocity, pen-tip pressure, timing) to form a judgment about the signer's identity. This approach's novelty lies in combining output from existing Neural Network and Hidden Markov Model based signature verification systems to improve the robustness of any specific approach used alone. The system performs reasonably well and achieves an overall error rate of 2:1% in the best case. The ...
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    This paper describes a system for performing handwritten signature verification using complementary statistical models. The system analyses both the static features of a signature (e.g., shape, slant, size), and its dynamic features (e.g., velocity, pen-tip pressure, timing) to form a judgment about the signer's identity. This approach's novelty lies in combining output from existing Neural Network and Hidden Markov Model based signature verification systems to improve the robustness of any specific approach used alone. The system performs reasonably well and achieves an overall error rate of 2:1% in the best case. The results of several other experiments are also presented including using less reference signatures, allowing multiple signing attempts, zero-effort forgery attempts, providing visual feedback, and signing a password rather than a signature.
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    Journal Title
    Journal of Computers
    Volume
    4
    Issue
    7
    DOI
    https://doi.org/10.4304/jcp.4.7.670-680
    Copyright Statement
    © 2009 Academy Publisher. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Information and computing sciences
    Information systems not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/48485
    Collection
    • Journal articles

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