SIMILARITY EVALUATION OF ONLINE SIGNATURES BASED ON MODIFIED DYNAMIC TIME WARPING
Saeid Rashidi, Ali Fallah, Farzad Towhidkhah · Applied Artificial Intelligence · 2013
Many people are very accustomed to the process of signing their name and having it matched for authentication. In a signature verification system, the signatures are processed to extract features that are used for verification. These features should not be duplicable. A basic problem is intraclass variations that will greatly affect the matching scores produced. The problem of distinctiveness occurs when the expectation of signatures to vary significantly between individuals is not met. There may be a large number of similarities in the feature sets used to represent the signatures of two different individuals. The efficiency of any signature verification system depends mainly on the discrimination power and robustness of the features used in the system. This study evaluates 40 functional features of viewpoint classification error and consistency for extracting the best subset once a set of features provides maximal discrimination capability between genuine and forged signatures. A modified distance of the DTW algorithm is proposed to improve performance of the 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, the average EER for the proposed algorithm in comparison with the general DTW algorithm shows a 5.47% decrease. Moreover, a comparative study based on a different classifier with a skilled forgery shows that the best result has an EER of 1.73% using the Parzen window classifier.