Similarity Measurement for Off-Line Signature Verification
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.
KeywordsTraining Sample Feature Point Signature Pattern Equal Error Rate Signature Verification
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- 4.Sabourin, R., Genest, G.: An Extended-shadow-code-based Approach for Off-line Signature Verification. Part I. Evaluation of The Bar Mask Definition. In: Proc. Int. Conference on Pattern Recognition, pp. 450–453 (1994)Google Scholar
- 7.Murshed, N., Sabourin, R., Bortolozzi, F.: A Cognitive Approach to Off-line Signature Verification. Automatic Bankcheck Processing. World Scientific Publishing Co., Singapore (1997)Google Scholar
- 8.O’Sullivan, F.: A Statistical Perspective on Ill-posed Inverse Problems. Statistical Science, 502–527 (1986)Google Scholar