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Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns.

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Summary
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This study introduces a novel eye-center localization method combining explicit shape regression and isophote curvature features. The approach achieves high accuracy and robustness, even for non-frontal faces, outperforming existing techniques.

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Area of Science:

  • Computer Vision
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Accurate eye-center localization is crucial for applications like facial recognition and neurological disorder screening.
  • Existing methods struggle with non-frontal face profiles, limiting their real-world applicability.
  • Robustness against scale and pose variations remains a significant challenge in eye-tracking systems.

Purpose of the Study:

  • To develop a robust and accurate eye-center localization method for both frontal and non-frontal faces.
  • To enhance the performance of facial analysis and gaze estimation systems.
  • To address the limitations of current eye-tracking techniques in handling diverse facial poses.

Main Methods:

  • A hybrid approach combining explicit shape regression for initial localization and isophote curvature features for refinement.
  • Utilizing the robustness of explicit shape regression against eye region changes as a constraint.
  • Employing isophote curvature features to identify candidate eye-center locations and minimize reconstruction error.

Main Results:

  • The proposed method demonstrates significant improvements in accuracy and robustness compared to state-of-the-art techniques.
  • Achieved second-rank accuracy in experimental evaluations on BioID and FERET datasets.
  • Successfully applied to non-frontal faces, a critical but often overlooked aspect in gaze estimation.

Conclusions:

  • The integration of explicit shape regression and isophote curvature analysis provides a powerful solution for accurate and robust eye-center localization.
  • The method's effectiveness on non-frontal faces opens new possibilities for advanced facial analysis and human-computer interaction.
  • The system achieves a practical frame rate of 38 Hz, suitable for real-time eye-tracking applications.