Off-line Signature Verification Incorporating the Prior Model

Abstract

The existing signature verification systems usually train classifiers for a new user by both his/her genuine and forgery signatures. Obviously, the requirement of forgery signatures is impractical. This paper presents an off-line signature verification system that only requires the genuine signatures of a new user. At the training stage the system learns the mapping between the parameters of classifiers without simple forgeries and those with simple forgeries. In the application stage, a primary classifier is trained for a new user without his/her simple forgeries. The final classifier is obtained by transforming the primary classifier via the mapping learnt in the training stage. Experimental results confirm the effectiveness of the proposed system.