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  • Off-line Signature Verification using G-SURF

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    84465_1.pdf (346.0Kb)
    Author(s)
    Pal, Srikanta
    Chanda, Sukalpa
    Pal, Umapada
    Franke, Katrin
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Pal, Srikanta
    Year published
    2012
    Metadata
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    Abstract
    In the field of biometric authentication, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an easy method for authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Signatures provide a secure means for confirmation and authorization in legal documents. So nowadays, signature identification and verification becomes an essential component in automating the rapid processing ...
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    In the field of biometric authentication, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an easy method for authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Signatures provide a secure means for confirmation and authorization in legal documents. So nowadays, signature identification and verification becomes an essential component in automating the rapid processing of documents containing embedded signatures. Sometimes, part-based signature verification can be useful when a questioned signature has lost its original shape due to inferior scanning quality. In order to address the above-mentioned adverse scenario, we propose a new feature encoding technique. This feature encoding is based on the amalgamation of Gabor filter-based features with SURF features (G-SURF). Features generated from a signature are applied to a Support Vector Machine (SVM) classifier. For experimentation, 1500 (50x30) forgeries and 1200 (50x24) genuine signatures from the GPDS signature database were used. A verification accuracy of 97.05% was obtained from the experiments.
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    Conference Title
    Proceedings of the 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)
    Publisher URI
    http://www.mirlabs.net/isda12
    DOI
    https://doi.org/10.1109/ISDA.2012.6416603
    Copyright Statement
    © 2012 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
    Image Processing
    Publication URI
    http://hdl.handle.net/10072/49615
    Collection
    • Conference outputs

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