The Catcher in the Eye: Recognizing Users by their Blinks
Abstract
In this paper, we develop a novel behavioral biometric recognition framework, BlinkAuth, that takes advantage of a user’s blinking. BlinkAuth utilizes electrooculogram (EOG) data, (i.e., the electric potential difference between the corneal and retinal sides of the eye), and applies a machine-learning model to achieve user recognition. BlinkAuth works with devices like smart glasses and VR headsets and can be used simultaneously in activities such as driving or cooking. Using JINS MEME, a glasses-type wearable device that can measure EOG, we collected EOG data from 31 participants under various conditions and evaluated the recognition accuracy of BlinkAuth. The results demonstrate that BlinkAuth can achieve high accuracy as a behavioral biometric recognition with an average AUC of 95.8% and an average EER of 9.28%. We developed a system for implementing BlinkAuth for real-time recognition and evaluated the time required for the recognition process and the system’s usability with the System Usability Scale (SUS). The results show an overall processing time of approximately 0.6 seconds, including the data measurement time, and an average SUS score of 82.50, which indicates high usability equivalent to rank A in the standard criteria for interpreting SUS scores. Six extensive user experiments and 17 evaluation perspectives reveal that BlinkAuth is highly robust to environmental changes, such as skin moisture and makeup, participant actions, and eye strain conditions, as well as to attacks that imitate the target’s blinking.