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

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Stephane Armand, Michael Blumenstein, Vallipuram Muthukkumarasamy
• Keywords: IJCNN 2006, Modified Direction Feature, Off-line Signature Verification, Unique Structural Features