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  • A Compact Size Feature Set for the Off-line Signature Verification Problem

    Author(s)
    Nguyen, Vu
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Nguyen, Vu
    Year published
    2012
    Metadata
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    Abstract
    With increasing computational power, researchers in the area of off-line signature verification have been able to investigate feature extraction techniques that produce large-dimensional feature vectors. However, a large feature vector is not necessarily associated with high performance. This paper investigates the performance of a small feature set consisting of 33 feature values. In the experiments using Support Vector Machines (SVMs), an average error rate (AER) of 16.80% was obtained together with a low false acceptance rate (FAR) for random forgeries of 0.19%. The significant reduction of the error rate was obtained ...
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    With increasing computational power, researchers in the area of off-line signature verification have been able to investigate feature extraction techniques that produce large-dimensional feature vectors. However, a large feature vector is not necessarily associated with high performance. This paper investigates the performance of a small feature set consisting of 33 feature values. In the experiments using Support Vector Machines (SVMs), an average error rate (AER) of 16.80% was obtained together with a low false acceptance rate (FAR) for random forgeries of 0.19%. The significant reduction of the error rate was obtained when the proposed global features were employed, which demonstrates their astonishingly high discriminant power. These results suggest that the use of global features for the off-line signature verification problem is worth further investigation.
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    Conference Title
    Proceedings 10th IAPR International Workshop on Document Analysis Systems DAS 2012
    Publisher URI
    http://www.ict.griffith.edu.au/das2012/
    DOI
    https://doi.org/10.1109/DAS.2012.1
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/52333
    Collection
    • Conference outputs

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