Similarity Measurement for Off-Line Signature Verification
- Cite this paper as:
- You X., Fang B., He Z., Tang Y. (2005) Similarity Measurement for Off-Line Signature Verification. In: Huang DS., Zhang XP., Huang GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg
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.
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