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Off-line signature verification based on grey level information using texture features

Off-line signature verification based on grey level information using texture features,10.1016/j.patcog.2010.07.028,Pattern Recognition,J. F. Vargas,M

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Off-line signature verification based on grey level information using texture features   (Citations: 5)
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A method for conducting off-line handwritten signature verification is described. It works at the global image level and measures the grey level variations in the image using statistical texture features. The co-occurrence matrix and local binary pattern are analysed and used as features. This method begins with a proposed background removal. A histogram is also processed to reduce the influence of different writing ink pens used by signers. Genuine samples and random forgeries have been used to train an SVM model and random and skilled forgeries have been used for testing it. Results are reasonable according to the state-of-the-art and approaches that use the same two databases: MCYT-75 and GPDS-100 Corpuses. The combination of the proposed features and those proposed by other authors, based on geometric information, also promises improvements in performance.
Journal: Pattern Recognition - PR , vol. 44, no. 2, pp. 375-385, 2011
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    • ...It is found from the literature that the existing off-line signature verification methods use signature image area, the signature height and width, the ratio between middle zone width and signature width, no. of characteristics points viz: end points, cross points, cusps and so on, number of loops, the presence of lower zone parts, the number of elements in the signature [1, 3, 4, 5, 6, 7, 8, 9, 14, 15, 18] as feature values...
    • ...In order to reveal the superiority of the verification accuracy of the proposed model, we have conducted experimentation on the grey level texture signature database [4] which is publicly available offline signature database...
    • ...In Table-2, we have also given the verification accuracy results obtained due to Vargas et al. [4] algorithm considering GLCM features proposed by the authors...
    • ...<{[SECTION]}>Table 2: Verification accuracy of the proposed model and Vargas et al [4] algorithm on texture signature database...
    • ...algorithm [4] [The dimension of the feature vector is 8 here] Dimension of feature vector...
    • ...J.F.Vargas et al., [4] 16.27 / 12.18 GLCM based featured...

    B. H. Shekar, et al. Eigen-signature: A robust and an efficient offline signature verificat...

    • ...An interesting tool for texture analysis that unifies both characteristics is the local binary pattern (LBP) [6] which has been applied in biometrics to the specific problems of face and hand recognition [7][8] and signature verification [9]...
    • ...It has been applied in biometrics to the specific problem of face and hand recognition [7][8] and signature verification [9]...

    Miguel A. Ferrer, et al. Off-line signature verification using local patterns

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