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  • An Application of the 2D Gaussian Filter for Enhancing Feature Extraction in Off-line Signature Verification

    Author
    Nguyen, Vu
    Blumenstein, Michael
    Year published
    2011
    Metadata
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    Abstract
    Abstract-Similar to many other pattern recognition problems, feature extraction contributes significantly to the overall performance of an off-line signature verification system. To be successful, a feature extraction technique must be tolerant to different types of variation whilst preserving essential information of input patterns. In this paper, we describe a grid-based feature extraction technique that utilises directional information extracted from the signature contour, i.e. the chain code histogram. Our experimental results for signature verification indicated that, by applying a suitable 2D Gaussian filter ...
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    Abstract-Similar to many other pattern recognition problems, feature extraction contributes significantly to the overall performance of an off-line signature verification system. To be successful, a feature extraction technique must be tolerant to different types of variation whilst preserving essential information of input patterns. In this paper, we describe a grid-based feature extraction technique that utilises directional information extracted from the signature contour, i.e. the chain code histogram. Our experimental results for signature verification indicated that, by applying a suitable 2D Gaussian filter on the matrices containing the chain code histograms, an average error rate (AER) of 13.90% can be obtained whilst maintaining the false acceptance rate (FAR) for random forgeries as low as 0.02%. These figures are comparable or better than those reported by other state of the art feature extraction techniques such as the Modified Direction Feature (MDF) and the Gradient feature.
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    Conference Title
    Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR 2011)
    Publisher URI
    http://www.icdar2011.org/EN/volumn/home.shtml
    Subject
    Image Processing
    Artificial Intelligence and Image Processing not elsewhere classified
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/42407
    Collection
    • Conference outputs

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