LockEyeGaze

LockEyeGaze uses gaze stimuli and tracking to create eye movement biometrics for user verification.

Year

8

Project

LockEyeGaze

University

University of St Andrews

URL

lockeyegaze.com

Market Need

LockEyeGaze confronts the cybersecurity challenge of sophisticated computer vision and 3D modelling technologies, such as deepfakes and AI-generated tampering, which have begun to erode the reliability of facial recognition as a secure authentication method.

The LockEyeGaze system utilises the dynamic patterns of eye movements for security, which are significantly more difficult to replicate than static biometric features like static face, iris and fingerprints.

Solution

The system utilises sensors integrated into devices, like front camera and IMU sensors, to capture the user’s eye movements. Three layers of authentication – eye movements recognition, deepfake attack detection, and face & pupil consistency authentication – protect the system.

LockEyeGaze’s algorithms map and analyse the unique patterns of eye movements and behaviour patterns as biometric, similar to fingerprints, enabling personalised and secure authentication. These algorithms are designed to adapt over time, enhancing security through continuous learning of the user’s eye movement and behaviour changes.

Target Market

  • Sectors with need for robust security and privacy protections, such as finance, healthcare and high-value consumer technology.
  • System integrators and multi-factor authentication providers.

Status

  • Minimum viable product developed.

Needs

  • Funding to scale development and build a sales team.
  • Further testing across user scenarios.
  • Certifications like NCSC essentials and other ISO standards.
  • Industry partnerships and pilot projects to make iterative improvements.
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