Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines

As a biometric, signatures have been widely used to identify people. In the context of static image processing, the lack of dynamic information such as velocity, pressure and the direction and sequence of strokes has made the realization of accurate off-line signature verification systems more challenging as compared to their on-line counterparts. In this paper, we propose an effective method to perform off-line signature verification based on intelligent techniques. Structural features are extracted from the signature's contour using the modified direction feature (MDF) and its extended version: the Enhanced MDF (EMDF). Two neural network-based techniques and Support Vector Machines (SVMs) were investigated and compared for the process of signature verification. The classifiers were trained using genuine specimens and other randomly selected signatures taken from a publicly available database of 3840 genuine signatures from 160 volunteers and 4800 targeted forged signatures. A distinguishing error rate (DER) of 17.78% was obtained with the SVM whilst keeping the false acceptance rate for random forgeries (FARR) below 0.16%.