Who You Are is How You Look: Implicit User Identification by Comparing Actual Gaze with Personalized Saliency Maps

Abstract

Traditional authentication methods, such as biometrics and passwords, cannot guarantee security once the authenticated user changes after login. Periodic re-authentication at short intervals could preserve security but would severely degrade usability. To balance security and usability, we propose an implicit and continuous identification method based on eye-tracking data. Our approach compares a saliency map personalized for the legitimate user with the gaze heatmap of the current user and identifies the current user as legitimate if the two maps are similar. To demonstrate our approach, we first develop and verify that a personalized saliency map well expresses a legitimate user’s gaze behavior. With one example result of the user study, we then illustrate the potential application of our identification as second-factor authentication and as an unobtrusive re-authentication suggestion.

Junichi Nagasawa

Contract Researcher

My research interests include Human-Computer Interaction, Security, eye tracking.