Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

An algorithm was developed to improve prediction of eye position from video-based eye tracker data. Eye trackers that determine eye position relying on images of pupil and corneal reflection positions typically make poor differentiation between changes in eye position and movements of the camera relative to the subject's head. The common method employed by video-based eye trackers to determine eye position involves calculation of the vector difference between the center of the pupil and the center of the corneal reflection under the assumption that the centers of the pupil and the corneal reflection change in unison when the camera moves with respect to the head. This assumption was tested and is shown to increase prediction error. Also, predicting the corneal reflection center is inherently less precise than that of the pupil due to the reflection's small size. Typical approaches thus generate eye positions that can only be as robust as the relatively noisy corneal reflection data. An algorithm has been developed to more effectively account for camera movements with respect to the head as well as reduce the noise in the final eye position prediction. This algorithm was tested and is shown to be particularly robust during the common situation when sharp eye movements occur intermixed with smooth head-to-camera changes.

REFERENCES

• 1: Jason S. Babcock , Jeff B. Pelz, Building a lightweight eyetracking headgear, Proceedings of the 2004 symposium on Eye tracking research & applications, p.109-114, March 22-24, 2004, San Antonio, Texas [doi>10.1145/968363.968386]
• 2: Carpenter, R. H. S. 1988. Movements of the Eyes (2nd ed.). Pion Limited, London.
• 3: Delabarre, E. B. 1898. A method of recording eye-movements. American Journal of Psychology 9, 4, 572--574.
• 4: Henderson, J. M., and Ferreira, F. 2004. The Interface of Language, Vision, and Action: Eye Movements and the Visual World. Pyschology Press.
• 5: ISCAN, INC. 2001. RK-726PCI Pupil/Corneal Reflection Tracking System, January.
• 6: Karmali, F., and Shelhamer, M. 2004. Automatic detection of camera translation in eye video recordings using multiple methods. In Proceedings of the 26th Annual International Conference of the IEEE EMBS, vol. 1, 1525--1528.
• 7: Land, M., Mennie, N., and Rusted, J. 1999. The roles of vision and eye movements in the control of activities of daily living. Perception 28, 1311--1328.
• 8: Pelz, J. B., and Canosa, R. 2001. Oculomotor behavior and perceptual strategies in complex tasks. Vision Research 41, 3587--3596.
• 9: Xie, X., Sudhakar, R., and Zhuang, H. 1998. A cascaded scheme for eye tracking and head movement compensation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 28, 4, 487--490.

INDEX TERMS

Primary Classification:
  D. Software
  D.1 PROGRAMMING TECHNIQUES
      D.1.1 Applicative (Functional) Programming

Additional Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.1 Numerical Algorithms and Problems

  G. Mathematics of Computing
  G.4 MATHEMATICAL SOFTWARE

Keywords:
algorithm, camera compensation, eye tracking, noise