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Multimedia Tools and Applications

, Volume 78, Issue 14, pp 19253–19278 | Cite as

Online handwritten signature verification based on association of curvature and torsion feature with Hausdorff distance

  • Lang He
  • Hua TanEmail author
  • Zhang-Can Huang
Article
  • 169 Downloads

Abstract

The paper presents an efficient on-line signature verification method based on the dynamic features of a given signature. In the proposed approach, curvature and torsion feature are associated with Hausdorff distance measure which can be used in the verification process. In the feature extraction step, the signature trajectory is approximated as a spatial curve. A set of curvature and torsion value of extreme point is computed from both x coordinate, y coordinate and pressure feature so that the dimension of the curve is reduced. Therefore, a new composed signature feature is created for each person. For the obtained feature data, the most distinctive Hausdorff distance is further proposed to calculate the distances of the eight-dimensional feature vector between the test signature and corresponding template signatures for the verification of the test sample. Comprehensive experiments are implemented on three publicly available databases: the SVC2004, SUSIG and MCYT-100 database. A comparison of our results with some recent signature verification methods available in the literature is provided with equal error rate, and the results indicate that the proposed method would better recognize genuine signatures, random and skilled forgeries.

Keywords

Online signature verification Extreme point Curvature Torsion Hausdorff distance 

Notes

Acknowledgements

This work was supported by National College Students Innovation and entrepreneurship training program in Wuhan University of Technology (Project No. 20161049714003). The authors would like to thank the reviewers for their invaluable comments and all the people who have provided their sample signatures used in this study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ScienceWuhan University of TechnologyWuhanChina

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