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Combining head pose and eye location information for gaze estimation.

Roberto Valenti1, Nicu Sebe, Theo Gevers

  • 1Intelligent Systems Lab, Amsterdam, University of Amsterdam, 1098 XH Amsterdam, The Netherlands. r.valenti@uva.nl

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 27, 2011
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Summary

This study introduces a hybrid system combining head pose and eye location for improved gaze estimation. The novel method enhances accuracy, especially in low-resolution videos and extreme head poses, outperforming traditional approaches.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Head pose and eye location estimation are crucial for gaze analysis but face challenges with non-frontal faces.
  • Existing eye localization methods struggle with accuracy in unconstrained settings.
  • Head pose estimation techniques are robust to variations in face orientation.

Purpose of the Study:

  • To propose a hybrid scheme integrating head pose and eye location for enhanced gaze estimation.
  • To improve the accuracy and operating range of eye localization, particularly in low-resolution videos.
  • To refine head pose tracking and develop a robust visual gaze estimation system.

Main Methods:

  • A hybrid scheme combining head pose and eye location information was developed.
  • Transformation matrices from head pose normalized eye regions; eye location matrices corrected pose estimation.
  • The system integrates enhanced eye location and head pose data for refined gaze estimates.

Main Results:

  • The unified scheme improved eye estimation accuracy by 16% to 23%.
  • The operating range for eye localization was extended by over 15°.
  • Head pose tracker accuracy improved by 12% to 24%, with mean gaze error between 2° and 5°.

Conclusions:

  • The proposed hybrid system significantly enhances gaze estimation accuracy and robustness.
  • This method overcomes limitations of traditional approaches, especially with extreme head poses and low-resolution video.
  • The system offers accurate gaze estimation without head position constraints, expanding its applicability.