Chapter

Advances in Intelligent Computing

Volume 3644 of the series Lecture Notes in Computer Science pp 272-281

Similarity Measurement for Off-Line Signature Verification

  • Xinge YouAffiliated withFaculty of Mathematics and Computer Science, Hubei UniversityDepartment of Computer Science, Hong Kong Baptist University
  • , Bin FangAffiliated withCenter for Intelligent Computation of Information, Chongqing UniversityDepartment of Computer Science, Hong Kong Baptist University
  • , Zhenyu HeAffiliated withDepartment of Computer Science, Hong Kong Baptist University
  • , Yuanyan TangAffiliated withFaculty of Mathematics and Computer Science, Hubei UniversityDepartment of Computer Science, Hong Kong Baptist University

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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.