An eye gaze-driven metric for estimating the strength of graphical passwords based on image hotspots

In this paper, we propose an eye gaze-driven metric based on hotspot vs. non-hotspot segments of images for unobtrusively estimating the strength of user-created graphical passwords by analyzing the users' eye gaze behavior during password creation. To examine the feasibility of this method, i.e., the existence of correlation between the proposed metric and the strength of users' generated passwords, we conducted an eye-tracking study (n=42), in which users created a graphical password with a personalized image that triggers declarative memory of users (familiar image) vs. an image illustrating generic content unfamiliar to the users' episodic and semantic memory (generic image). Results revealed a strong positive correlation between the password strength and the proposed eye gaze-driven metric, pointing towards a new direction for the design of intelligent eye gaze-driven graphical password schemes for unobtrusively assisting users in making better password choices.