Creation and selection of most stable discriminating features for on-line signature verification

In this paper, we propose a method for exploiting the dependence of four dynamic features (horizontal trajectory, vertical trajectory, pressure and velocity) upon angles of each sample point from the mean of the signature. We have achieved this purpose by decomposing each of these four dynamic features into two partitions. As a result we obtain eight discriminating features, out of which we have selected two most stable features on the basis of training signatures scatter on two dimensional dissimilarity space. These two most stable features are then employed for verification purpose. Result shows the promise of our proposed technique.