An Information Analysis of In-Air and On-Surface Trajectories in Online Handwriting

This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer recognition and that they can be effectively combined to enhance the recognition accuracy. With samples from the BiosecurID database, we have analysed the entropy of each kind of trajectories, as well as the amount of information they share, and the difference between intra- and inter-writer measures of the mutual information. The results show that when pressure is not taken into account, the amount of information is similar in both types of trajectories. Furthermore, even if they share some information, in-air and on-surface trajectories appear to be notably non-redundant.