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Pattern Recognition
Volume 37, Issue 1, January 2004, Pages 93-103
 
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doi:10.1016/S0031-3203(03)00229-2    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2003 Pattern Recognition Society. Published by Elsevier Science B.V.

Using geometric extrema for segment-to-segment characteristics comparison in online signature verification

Jaeyeon LeeCorresponding Author Contact Information, E-mail The Corresponding Author, 1, Ho-Sub YoonE-mail The Corresponding Author, Jung SohE-mail The Corresponding Author, Byung Tae ChunE-mail The Corresponding Author and Yun Koo ChungE-mail The Corresponding Author

ETRI, Computer Software Technology Lab, 161 Gajeong-dong, Yusung-gu, Daejeon, 305-350, South Korea

Received 27 November 2002; 
accepted 26 June 2003. ;
Available online 27 August 2003.

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Abstract

A signature verification algorithm based on a segment-to-segment matching is described, in which a new approach for finding segment-to-segment correspondence is proposed. In the proposed method, geometric extrema are used as segmenting boundaries with two reasons: (1) extrema are reproduced with high stability and (2) the properties of extrema are useful in finding segment-to-segment correspondence. In this paper, a set of rules for legitimate correspondence between extrema is defined based on their properties, which is utilized for the similarity evaluation between segments. Dynamic programming is then applied to find an optimal correspondence map. Through experiments, the proposed algorithm is assessed to be effective in improving discriminative ability. We also showed that the overall performance is further improved by combining the proposed method with the traditional global parametric algorithm.

Author Keywords: Biometrics; Security; Signature; Verification; Segmentation; Segment-to-segment correspondence; Dynamic programming

Article Outline

1. Introduction
2. Using geometric extrema for segmentation
3. Matching based on geometric extrema
3.1. Extraction of geometric extrema
3.2. Constructing correspondence map between extrema lists
3.3. Similarity evaluation
3.4. Feature extraction and classification
4. Experimental system implementation
4.1. The first system—Global parametric algorithm
4.2. The second system—Extrema matching algorithm
4.3. The third system—Combination of global parametric algorithm and extrema matching
5. Experiments
5.1. Experimental database
5.2. Experiments on implemented systems in the context of random forgery
5.3. Experiments in the context of skilled forgery
6. Conclusion
References
Vitae






Pattern Recognition
Volume 37, Issue 1, January 2004, Pages 93-103
 
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