A Cognitive Approach to Off-Line Signature Verification
This paper presents an Off-line Signature Verification System for identifying random forgeries aimed at banking application. The cognitive information learning process in the proposed system is inspired by some characteristics of human learning. Four features distinguish the proposed system from those proposed thus far. First, the verification task is accomplished without a priori knowledge of the class of random forgeries. Second, no explicit modeling or making geometrical measurements are used to represent the signature. Third, the decision of the system is made throughout the use of two-stage verification process by which a global and/or local analysis are performed on the unknown signature. The global analysis is concerned with the overall shape of the unknown signature, whereas, the local analysis is concerned with the local features composing the unknown signature. Fourth, these analysis are performed at the boundary or within a predefined search region called the identity grid designed for each writer in the system. The proposed system is evaluated with a data base of 800 signatures.