Abstract
This paper proposes a novel off-line signature verification method based on adaptive multi-resolution wavelet zero-crossing and one-class-one-network classification. First, the horizontal, vertical, 45 degree direction and the 135 degree direction projections of the binarizated signature images are calculated, respectively. The curvature data of the projections are decomposed into multi-resolution signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for verification. At last, one-class-one-network classifier is used to verify the signatures. The signature verification system was experimented on real data sets and the results show the system is very effective.
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© 2007 Springer Berlin Heidelberg
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Ma, Z., Zeng, X., Zhang, L., Li, M., Zhou, C. (2007). A Novel Off-Line Signature Verification Based on Adaptive Multi-resolution Wavelet Zero-Crossing and One-Class-One-Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_131
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DOI: https://doi.org/10.1007/978-3-540-72395-0_131
Publisher Name: Springer, Berlin, Heidelberg
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