Motion tracking of iris features to detect small eye movements

  • Aayush K. Chaudhary Rochester Institute of Technology
  • Jeff B. Pelz Rochester Institute of Technology
Keywords: Microsaccades, eye movements, eye tracking methodology, iris features, visual fixations, video-based eye tracking, head motion compensation, iris segmentation


The inability of current video-based eye trackers to reliably detect very small eye movements has led to confusion about the prevalence or even the existence of monocular microsaccades (small, rapid eye movements that occur in only one eye at a time). As current methods often rely on precisely localizing the pupil and/or corneal reflection on successive frames, current microsaccade-detection algorithms often suffer from signal artifacts and a low signal-to-noise ratio. We describe a new video-based eye tracking methodology which can reliably detect small eye movements over 0.2 degrees (12 arcmin) with very high confidence. Our method tracks the motion of iris features to estimate velocity rather than position, yielding a better record of microsaccades. We provide a more robust, detailed record of miniature eye movements by relying on more stable, higher-order features (such as local features of iris texture) instead of lower-order features (such as pupil center and corneal reflection), which are sensitive to noise and drift.

Author Biographies

Aayush K. Chaudhary, Rochester Institute of Technology
Ph.D. student at Carlson Center of Imaging Science
Jeff B. Pelz, Rochester Institute of Technology
Professor at Carlson Center of Imaging Science
How to Cite
Chaudhary, A. K., & Pelz, J. B. (2019). Motion tracking of iris features to detect small eye movements. Journal of Eye Movement Research, 12(6).
Special Thematic Issue: „Microsaccades: Empirical Research and Methodological Advances“