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ABSTRACT

We present a multi-camera vision-based eye tracking method to robustly locate and track user's eyes as they interact with an application. We propose enhancements to various vision-based eye-tracking approaches, which include (a) the use of multiple cameras to estimate head pose and increase coverage of the sensors and (b) the use of probabilistic measures incorporating Fisher's linear discriminant to robustly track the eyes under varying lighting conditions in real-time. We present experiments and quantitative results to demonstrate the robustness of our eye tracking in two application prototypes.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.m Miscellaneous

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Probabilistic algorithms (including Monte Carlo)

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.0 General
          Subjects: Image processing software

General Terms:
Algorithms, Design, Human Factors

Keywords:
Fisher's Discriminant Analysis, computer vision, eye tracking, human computer interaction, multiple cameras