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Similarity Measurement for Off-Line Signature Verification

  • Xinge You
  • Bin Fang
  • Zhenyu He
  • Yuanyan Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3644)

Abstract

Existing methods to deal with off-line signature verification usually adopt the feature representation based approaches which suffer from limited training samples. It is desired to employ straightforward means to measure similarity between 2-D static signature graphs. In this paper, we incorporate merits of both global and local alignment methods. Two signature patterns are globally registered using weak affine transformation and correspondences of feature points between two signature patterns are determined by applying an elastic local alignment algorithm. Similarity is measured as the mean square of sum Euclidean distances of all found corresponding feature points based on a match list. Experimental results showed that the computed similarity measurement was able to provide sufficient discriminatory information. Verification performance in terms of equal error rate was 18.6% with four training samples.

Keywords

Training Sample Feature Point Signature Pattern Equal Error Rate Signature Verification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xinge You
    • 1
    • 3
  • Bin Fang
    • 2
    • 3
  • Zhenyu He
    • 3
  • Yuanyan Tang
    • 1
    • 3
  1. 1.Faculty of Mathematics and Computer ScienceHubei UniversityChina
  2. 2.Center for Intelligent Computation of InformationChongqing UniversityChongqingChina
  3. 3.Department of Computer ScienceHong Kong Baptist University 

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