Signature identification and verification has been a topic of interest and importance for many years in the area of biometrics. In this paper we present an effective method to perform off-line signature verification and identification. To commence the process, the signature's contour is first determined from its binary representation. Unique structural features are subsequently extracted from the signature's contour through the use of a novel combination of the Modified Direction Feature (MDF) in conjunction with additional distinguishing features to train and test two Neural Network-based classifiers. A Resilient Back Propagation neural network and a Radial Basis Function neural network were compared. Using a publicly available database of 2106 signatures containing 936 genuine and 1170 forgeries, we obtained a verification rate of 91.12%.
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