Hide my Gaze with EOG! Towards Closed-Eye Gaze Gesture Passwords that Resist Observation-Attacks with Electrooculography in Smart Glasses

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units


Smart glasses allow for gaze gesture passwords as a hands-free form of mobile authentication. However, pupil movements for password input are easily observed by attackers, who thereby can derive the password. In this paper we investigate closed-eye gaze gesture passwords with EOG sensors in smart glasses. We propose an approach to detect and recognize closed-eye gaze gestures, together with a 7 and 9 character gaze gesture alphabet. Our evaluation indicates good gaze gesture detection rates. However, recognition is challenging specifically for vertical eye movements with 71.2%-86.5% accuracy and better results for opened than closed eyes. We further find that closed-eye gaze gesture passwords are difficult to attack from observations with 0% success rate in our evaluation, while attacks on open eye passwords succeed with 61%. This indicates that closed-eye gaze gesture passwords protect the authentication secret significantly better than their open eye counterparts.


Original languageEnglish
Title of host publication17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019)
Publication statusAccepted/In press - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Advances in Mobile Computing and Multimedia - Munich, Germany
Duration: 2 Dec 20194 Dec 2019
Conference number: 17


ConferenceInternational Conference on Advances in Mobile Computing and Multimedia
Abbreviated titleMoMM

    Research areas

  • authentication, closed-eye, EOG sensors, hands-free, gaze gestures, mobile, password, smart glasses

ID: 37133254