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A Novel Sigma-Lognormal Parameter Extractor for Online Signatures

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12823))

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Abstract

Online signature analysis can be widely applied in e-security and health. The latest method combines the Sigma-Lognormal model and visual feedback to extract the kinematic and spatial parameters of online signatures, but the model still does not perform well in complex handwriting signatures. Inaccurate parameters cannot reveal health information about users and cannot correctly reconstruct the online signature. This paper presents a novel Sigma-Lognormal parameter extractor for this drawback. On the one hand, this extractor estimates the parameters of pen-up and optimizes the parameters without the stroke midpoint. On the other hand, the extractor dynamically corrects the salient point position deviation by the velocity minimum point and velocity intersection point of adjacent strokes. The new extractor solves the parameter distortion caused by the ignored pen-ups and the hidden time deviation. The experiments demonstrate the accuracy and robustness of our method on multiple databases and verifiers, and the results show that the performance of the new extractor is better than the state-of-the-art method.

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, China

    Jianhuan Huang & Zili Zhang

Authors
  1. Jianhuan Huang
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  2. Zili Zhang
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Corresponding author

Correspondence to Zili Zhang .

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Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Huang, J., Zhang, Z. (2021). A Novel Sigma-Lognormal Parameter Extractor for Online Signatures. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_30

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  • DOI: https://doi.org/10.1007/978-3-030-86334-0_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86333-3

  • Online ISBN: 978-3-030-86334-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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