A Novel Off-Line Signature Verification Based on Adaptive Multi-resolution Wavelet Zero-Crossing and One-Class-One-Network

  • Authors:

  • Zhiqiang Ma;Xiaoyun Zeng;Lei Zhang;Meng Li;Chunguang Zhou

  • Affiliations:

  • College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
  • Year:
  • 2007

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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.