Identity authentication using improved online signature verification method

We present a system for online handwritten signature verification, approaching the problem as a two-class pattern recognition problem. A test signature’s authenticity is established by first aligning it with each reference signature for the claimed user, using dynamic time warping. The distances of the test signature to the nearest, farthest and template reference signatures are normalized by the corresponding mean values obtained from the reference set, to form a three-dimensional feature vector. This feature vector is then classified into one of the two classes (genuine or forgery). A linear classifier used in conjunction with the principal component analysis obtained a 1.4% error rate for a data set of 94 people and 619 test signatures (genuine signatures and skilled forgeries). Our method received the first place at SVC2004 with a 2.8% error rate.