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Low-cost Geometry-based Eye Gaze Detection using Facial Landmarks Generated through Deep Learning
  • +2
  • Run Zhou Ye,
  • Esther Enhui,
  • John Enzhou,
  • Joseph Ye,
  • Jacob Ye
Run Zhou Ye

Corresponding Author:[email protected]

Author Profile
Esther Enhui
John Enzhou
Joseph Ye
Jacob Ye


Introduction: In the realm of human-computer interaction and behavioral research, accurate real-time gaze estimation is critical. Traditional methods often rely on expensive equipment or large datasets, which are impractical in many scenarios. This paper introduces a novel, geometrybased approach to address these challenges, utilizing consumer-grade hardware for broader applicability. Methods: We leverage novel face landmark detection neural networks capable of fast inference on consumer-grade chips to generate accurate and stable 3D landmarks of the face and iris. From these, we derive a small set of geometry-based descriptors, forming an 8-dimensional manifold representing the eye and head movements. These descriptors are then used to formulate linear equations for predicting eye-gaze direction. Results: Our approach demonstrates the ability to predict gaze with an angular error of less than 1.9 degrees, rivaling state-of-the-art systems while operating in real-time and requiring negligible computational resources. Conclusion: The developed method marks a significant step forward in gaze estimation technology, offering a highly accurate, efficient, and accessible alternative to traditional systems. It opens up new possibilities for real-time applications in diverse fields, from gaming to psychological research.