University of Houston

Due to the dramatical increase in popularity of mobile devices in the last decade, more sensitive user information is stored and accessed on these devices everyday. Securing the sensitive data stored and accessed from mobile devices makes user identity management a problem of paramount importance. The tension between security and usability renders however the task of user identity verification on mobile devices a challenging task. However, most existing technologies for user identity verification only cover the login stage or only work in restricted controlled environments or GUIs in the post login stage, and most of them lack of application level access control management.

To solve the aforementioned problems, we investigate and seek solutions from both software and hardware aspects. For the software aspect, we employed the data collected from onboard sensors, including voice from microphone, acceleration data from accelerometer, angular acceleration from gyroscope, magnetic force from magnetometer, and multi-touch gesture input from touchscreen. Both biometric and behaviour features are extracted from the above collected data and different machine learning and pattern recognition methods are employed to perform user identity verification based on these features. For the hardware aspect, we research on solving identity verification problem employing touch panel with integrated fingerprint sensors on both local and remote scenarios. User study and on device testing sessions are held for privacy and usability evaluation.