A Contourlet-Based Method for Handwritten Signature Verification

Handwritten Signature Verification (HSV) is a discipline which aims to validate the identity of writers according to the handwriting styles. Compared with on-line HSV, off-line HSV is less limited in equipment involvement and can be applied in more fields. Nevertheless, it is more difficult to manipulate than on-line HSV due to the loss of dynamic information during the writing process, such as writing position, velocity, acceleration and pressure. In this paper, we focus on off-line HSV and present a new feature selection method based on Contourlet, which gives full play to the merits of both conventional structure feature and statistical feature. After dimensionality reduction to extracted eigenvector by K-L transform, genuine signatures and forgeries are distinguished through support vector machines (SVM). The result of our experiment has confirmed the effectiveness of the proposed approach.