Hide my Gaze with EOG! Towards Closed-Eye Gaze Gesture Passwords that Resist Observation-Attacks with Electrooculography in Smart Glasses. Findling, R. D., Quddus, T., & Sigg, S. In 17th International Conference on Advances in Mobile Computing and Multimedia, 2019.
Hide my Gaze with EOG! Towards Closed-Eye Gaze Gesture Passwords that Resist Observation-Attacks with Electrooculography in Smart Glasses [pdf]Paper  abstract   bibtex   1 download  
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.
@InProceedings{Findling_19_HidemyGaze,
  author    = {Rainhard Dieter Findling and Tahmid Quddus and Stephan Sigg},
  booktitle = {17th International Conference on Advances in Mobile Computing and Multimedia},
  title     = {Hide my Gaze with {EOG}! {T}owards Closed-Eye Gaze Gesture Passwords that Resist Observation-Attacks with Electrooculography in Smart Glasses},
  year      = {2019},
  abstract  = {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.},
  url_Paper = {http://ambientintelligence.aalto.fi/paper/findling_closed_eye_eog.pdf},
  project = {hidemygaze},
  group = {ambience}
  }
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