VIDENS: Vision-based User Identification from Inertial Sensors

Abstract

In this paper we propose the VIDENS (vision-based user identification from inertial sensors) approach, which transforms inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad-hoc deep learning models that combine RNNs and CNNs for user identification. Our evaluation shows promising results when comparing our approach to some relevant existing methods.