Off-line Signature Verification Based On One-Class-One-Neural Network

Handwritten signature is one of the most widely accepted personal attributes for identity verification. As a symbol of consent and authorization, especially in the prevalence of credit cards and band cheques, handwritten signature has long been the target of fraudulence. Therefore, with the growing demand for processing of individual identification faster and more accurately, the design of an automatic signature system faces a real challenge.Handwritten signature verification can be divided into on-line (or dynamic) and off-line (or static) signature verification. On-line verification refers to a process that the signer uses a special pen called a stylus to create his or her signature, producing the pen locations, speeds and pressures, while off-line verification just deals with signature images acquired by a scanner or a digital camera.In general, off-line signature verification is a challenging problem. Unlike the on-line signature, where dynamic aspects of the signing action are captured directly when handwriting. The dynamic information contained in off-line signature is highly degraded.On the base of other studies, we put forward four groups novel features including direction features, texture features, dynamic features and geometry features. Here, the direction features are the direction distribution of the foregroud pixels and the direction of the crossings and the inflexions; we get the texture features of the signature by the cross-diagonal texture matrix (CDTM); the dynamic features are the gray distribution of the foregroud pixels and the four features about high pressure regions; the geometry features are the density of the signature and the complexity index.In addition, The curvature data of the four projections of the signatures 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.