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Hide my gaze!

Hide My Gaze! investigates closed-eye gaze gesture password input to smart glasses for mobile authentication. It senses closed-eye gaze gestures with cameras and/or electrooculography (EOG) sensors built into the frames of the glasses. The advantage of closed-eye gaze gesture passwords over regular, open-eyed gaze is that observing the password - which is composed from user’s eye movements - is significantly more difficult for attackers who are able to observe the user’s eyes during password input. If eyes are opened attackers can observe pupil movements. However, if eyes are closed, attackers can only observe eye movements through the eyelids.

Research questions

Hide My Gaze! asks and answers three core research questions of employing closed-eye gaze gesture passwords for mobile authentication with cameras and EOG sensors in smart glasses:

(1) How well can closed-eye gaze gestures be recognized with cameras and EOG sensors built into smart glasses frames?

(2) How well can closed-eye gaze gesture passwords be attacked over their opened-eye counterparts, given attackers who are able to observe the user’s eyelid movements or pupil movements during password input?

(3) How do users and attackers see the usability and attackability of closed-eye and opened-eye gaze gesture passwords?

Datasets

The Hide My Gaze! dataset contains sub-datasets for camera and EOG data as well as for data with closed and opened eyes.

Camera dataset

The Hide My Gaze! camera dataset contains 81 password samples by 15 participants.

Each participant could choose two self-created gaze passwords, each containing 4-6 gaze gestures as password characters. Each participant performed each of their two passwords 2-5 times, both with closed and opened eyes. The recording device was a Pupil Labs first generation Pupil Eye Tracker which has the form factor of smart glasses. We utilized a camera resolution of 320x200 pixel with 120Hz sampling rate. For privacy reasons, as the iris is visible in camera recordings with opened eyes, the video part of the opened-eye dataset is not made publicly available.

Camera: closed eyes

Original videos [TBA]: contains mp4 video recordings of camera-based closed-eye gaze gesture password samples, alongside one csv metadata file providing basic information about the passwords.

Extracted eye movements [TBA]: contains csv files with eye movements timeseries from the videos above, extracted with optical flow, alongside an extended version of the corresponding metadata csv file.

Camera: opened eyes

Original Pupil gaze directions [TBA]: contains Pupil gaze direction recordings csv files of camera-based opened-eye gaze gesture password samples, alongside one csv metadata file providing basic information about the passwords.

Extracted eye movements [TBA]: contains csv files with eye movements timeseries from the videos above, extracted with optical flow, alongside an extended version of the corresponding metadata csv file.

EOG dataset

The Hide My Gaze! EOG dataset contains 81 password samples by 15 participants, containing a total of 380 gaze gestures. Each participant could choose two self-created gaze passwords, each containing 4-5 gaze gestures as password characters. Each participant performed each of their two passwords 2-5 times, both with closed and opened eyes. The recording device was first generation JINS MEME ES_R smart glasses. We utilized an EOG sensor sampling rate of 120Hz and 12bit quantization.

EOG: closed eyes
Extracted eye movements [7z]: contains csv files with eye movement timeseries from the JINS MEME smart glasses, alongside one metadata csv with filenames, subject IDs, and password sequences.

EOG: opened eyes
Extracted eye movements [7z]: contains csv files with eye movement timeseries from the JINS MEME smart glasses, alongside one metadata csv with filenames, subject IDs, and password sequences.

Publications

  2019 (3)
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.; and 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   bibtex   abstract
Free-Form Gaze Passwords from Cameras Embedded in Smart Glasses. Friström, E.; Lius, E.; Ulmanen, N.; Hietala, P.; Kärkkäinen, P.; Mäkinen, T.; Sigg, S.; and Findling, R. D. In 17th International Conference on Advances in Mobile Computing and Multimedia, 2019.
Free-Form Gaze Passwords from Cameras Embedded in Smart Glasses [pdf] paper   bibtex   abstract
Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses. Findling, R. D.; Nguyen, L. N.; and Sigg, S. In International Work-Conference on Artificial Neural Networks, 2019.
Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses [pdf] paper   doi   bibtex   abstract

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