Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classification

Signatures continue to be an important biometric for authenticating the identity of human beings. This paper presents an effective method to perform off-line signature verification using unique structural features extracted from the signature's contour. A novel combination of the modified direction feature (MDF) and additional distinguishing features such as the centroid, surface area, length and skew are used for classification. A resilient backpropagation (RBP) neural network and a radial basis function (RBF) network were compared in terms of verification accuracy. Using a publicly available database of 2106 signatures (936 genuine and 1170 forgeries), verification rates of 91.21% and 88.0% were obtained using RBF and RBP respectively.