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  • An Efficient Signature Verification Method based on an Interval Symbolic Representation and a Fuzzy Similarity Measure

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    Accepted Manuscript (AM)
    Author(s)
    Alaei, Ali Reza
    Pal, Srikanta
    Pal, Umapada
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Pal, Srikanta
    Alaei, Ali Reza R.
    Year published
    2017
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    Abstract
    In this paper an efficient off-line signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of Local Binary Pattern (LBP) based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual’s handwritten signature class. A novel fuzzy similarity measure is further proposed to ...
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    In this paper an efficient off-line signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of Local Binary Pattern (LBP) based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual’s handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark off-line English signature dataset (GPDS-300) and a large dataset (BHSig260) composed of Bangla and Hindi off-line signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.
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    Journal Title
    IEEE Transactions on Information Forensics and Security
    Volume
    PP
    Issue
    99
    DOI
    https://doi.org/10.1109/TIFS.2017.2707332
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Information and Computing Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/340668
    Collection
    • Journal articles

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