Authentication based on signature verification using position, velocity, acceleration and Jerk signals

Signature verification techniques utilize many different characteristics of an individual. The selection of signature features is critical in determining the performance of a signature verification system. Even though it is critical to select a suitable set of features to be extracted, emphasis has to be put into selecting an appropriate classifier for the features selected. This paper evaluates 19 dynamic features viewpoint classification error and discrimination capability between genuine and forgery signatures. A modified distance of DTW algorithm is proposed to improve performance of verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity-based. Second, average EER for proposed algorithm in comparison with the general DTW algorithm show a relative decrease 46.4%.