Towards a Continuous User Authentication Using Haptic Information

Abstract

With the advancement in multimedia systems and the increased interest in haptics to be used in interpersonal communication systems, where users can see, show, hear, tell, touch and be touched, mouse and keyboard are no longer dominant input devices. Touch, speech and vision will soon be the main methods of human computer interaction. Moreover, as interpersonal communication usage increases, the need for securing user authentication grows. In this research, we examine a user's identification and verification based on haptic information. We divide our research into three main steps. The first step is to examine a pre-defined task, namely a handwritten signature with haptic information. The user target in this task is to mimic the legitimate signature in order to be verified. As a second step, we consider the user's identification and verification based on user drawings. The user target is predefined, however there are no restrictions imposed on the order or on the level of details required for the drawing. Lastly, we examine the feasibility and possibility of distinguishing users based on their haptic interaction through an interpersonal communication system. In this third step, there are no restrictions on user movements, however a free movement to touch the remote party is expected. In order to achieve our goal, many classification and feature reduction techniques have been discovered and some new ones were proposed. Moreover, in this work we utilize evolutionary computing in user verification and identification. Analysis of haptic features and their significance on distinguishing users is hence examined.

The results show a utilization of visual features by Genetic Programming (GP) towards identity verification, with a probability equal to 50% while the remaining haptic features were utilized with a probability of approximately 50%. Moreover, with a handwritten signature application, a verification success rate of 97.93% with False Acceptance Rate (FAR) of 1.28% and @11.54% False Rejection Rate (FRR) is achieved with the utilization of genetic programming enhanced with the random over sampled data set. In addition, with a totally free user movement in a haptic-enabled interpersonal communication system, an identification success rate of 83.3% is achieved when random forest classifier is utilized.