  This paper studies continuous authentication for touch interface based mobile
devices. A Hidden Markov Model (HMM) based behavioral template training
approach is presented, which does not require training data from other subjects
other than the owner of the mobile. The stroke patterns of a user are modeled
using a continuous left-right HMM. The approach models the horizontal and
vertical scrolling patterns of a user since these are the basic and mostly used
interactions on a mobile device. The effectiveness of the proposed method is
evaluated through extensive experiments using the Toucha-lytics database which
comprises of touch data over time. The results show that the performance of the
proposed approach is better than the state-of-the-art method.
