On the Accuracy of Eye Gaze-driven Classifiers for Predicting Image Content Familiarity in Graphical Passwords

Abstract

Graphical passwords leverage the picture superiority effect to enhance memorability, and reflect today's haptic users' interaction realms. Images related to users' past sociocultural experiences (e.g., retrospective) enable the creation of memorable and secure passwords, while randomly system-assigned images (e.g., generic) lead to easy-to-predict hotspot regions within graphical password schemes. What remains rather unexplored is whether the image type could be inferred during the password creation. In this work, we present a between-subjects user study in which 37 participants completed a recall-based graphical password creation task with retrospective and generic images, while we were capturing their visual behavior. We found that the image type can be inferred within a few seconds in real-time. User adaptive mechanisms might benefit from our work's findings, by providing users early feedback whether they are moving towards the creation of a weak graphical password.