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Eye tracking method based on correlation and detected eye movement

  • US 7,331,671 B2
  • Filed: 03/29/2004
  • Issued: 02/19/2008
  • Est. Priority Date: 03/29/2004
  • Status: Active Grant
First Claim
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1. A method of tracking movement of a subject'"'"'s eye between first and second successively generated video images after a position of the subject'"'"'s eye in said first video image has been identified, comprising the steps of:

  • defining a state vector for the first video image corresponding to the identified position of the subject'"'"'s eye;

    defining an eye template in said second video image based on said state vector, and defining a search window comprising said eye template and a portion of the second video image surrounding said eye template;

    forming a difference image corresponding to differences between said search window and a corresponding portion of said first video image;

    identifying at least one eye movement candidate region in the difference image;

    determining a centroid of the eye movement candidate region and extracting a patch from the search window based on the determined centroid;

    when the extracted search window patch has the appearance of an eye, identifying eyelid motion between the first and second video images and updating the state vector for the second video image according to the determined centroid; and

    when the extracted search window patch does not have the appearance of an eye, identifying a lack of eyelid motion between the first and second video images, and updating the state vector for the second video image according to the following steps;

    computing correlation values based on a comparison of said eye template with different regions of said search window, and selecting a first region for which the computed correlation value is highest;

    establishing an eye model defining image characteristics of the subject'"'"'s eye and a non-eye model defining image characteristics of facial features other than the subject'"'"'s eye;

    computing deviations of the search window regions from said eye model, and selecting a second region for which the computed deviation is lowest;

    updating the state vector for the second video image according to a center of the first selected region if said first selected region is determined to be more reliable than said second selected region; and

    updating the state vector for the second video image according to a center of the second selected region if said second selected region is determined to be more reliable than said first selected region.

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