A Novel Off-line Signature Verification Based on One-class-one-network

This paper proposes a novel off-line signature verification method based on one-class-one-network classification, using four groups features. The features include direction features, texture features, dynamic features and complexity index. 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 effective with the average error rate can reach 1.8%, which is obviously satisfactory.

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