On the discrimination power of dynamic features for online signature

The mobile market has taken huge leap in the last two decades, re-defining the rules of communication, networking, socializing and transactions among individuals and organizations. Authentication based on verification of signature on mobile devices, is slowly gaining popularity. Most online signature verification algorithms focus on computing the global Equal Error Rate (EER) across all users for a dataset. In this work, contrary to such a representation, it is proposed that there are user-specific differences on the combined features and user-specific differences on each feature on the Equal Error Rate (EER) values. The experiments to test the hypothesis is tested on the two publicly available benchmark datasets using the dynamic time warping (DTW) algorithm. From the experiments, it is observed that for the MCYT-100 dataset, which yields an overall EER of 8%, the range of user-specific EER is between 0% and 27%.