Touch Gesture-Based Active User Authentication Using Dictionaries

Screen touch gesture has been shown to be a promising modality for touch-based active authentication of users of mobile devices. In this paper, we present an approach for active user authentication using screen touch gestures by building linear and kernelized dictionaries based on sparse representations and associated classifiers. Experiments using a new dataset collected by us as well as two other publicly available screen touch datasets show that the dictionary-based classification method compares favorably to those published in the literature. Experiments done using data collected in three different sessions corresponding to different environmental conditions show a drop in performance when the training and test data come from different sessions. This suggests a need for applying domain adaptation methods to further improve the performance of the classifiers.