Finding an Efficient Threshold for Fixation Detection in Eye Gaze Tracking

  • Sudarat TangnimitchokEmail author
  • Nonnarit O-larnnithipong
  • Armando Barreto
  • Francisco R. Ortega
  • Naphtali D. Rishe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9732)


We propose a combined analytical/statistical method to determine an efficient threshold on the dispersion of estimates of the point of gaze (POG) to indicate a user fixation. The experimental data for this study was obtained with an EyeTech TM3 eye gaze tracker (EGT). The experimental protocol to make the user fixate on pre-determined visual targets was implemented using the C language and OpenCV. Subjects first used the system in a training mode, from which an individualized dispersion threshold was obtained. Our approach was verified by applying the individualized threshold to POG data from a second run, in testing mode, with encouraging results.


Fixation identification Eye gaze tracking Data analysis algorithm 



This material is based in part upon work supported by the National Science Foundation under Grant Nos. I/UCRC IIP-1338922, AIR IIP-1237818, SBIR IIP-1330943, III-Large IIS-1213026, MRI CNS-1532061, OISE 1541472, MRI CNS-1532061, MRI CNS-1429345, MRI CNS-0821345, MRI CNS-1126619, CREST HRD-0833093, I/UCRC IIP-0829576, MRI CNS-0959985, RAPID CNS-1507611.


  1. 1.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCV library. O’Reilly, Sebastopol, CA (2008)Google Scholar
  2. 2.
    Duchowski, A.: Eye tracking methodology: Theory and practice, 2nd edn. Springer, Heidelberg (2009)zbMATHGoogle Scholar
  3. 3.
    Jacob, R.J.K.: What you look at is what you get: Eye movement based interaction techniques. Proceedings ACMCHI’90 Human Factors in Computing Systems, pp. 11–18. ACM Press, New York (1990)Google Scholar
  4. 4.
    Jacob, R.J.K.: Eye movement–based human–computer interaction techniques: Toward noncommand interfaces. In: Hartson, H.R., Hix, D. (eds.) Advances in human–computer interaction, vol. 4, pp. 151–190. Ablex, Norwood, NJ (1993)Google Scholar
  5. 5.
    Kumar, M., Klingner, J., Puranik, R., Winograd, T., Paepcke, A.: Improving the accuracy of gaze input for interaction. Proceedings of the 2008 Symposium on Eye Tracking Research and Applications, pp. 65–68. ACM Press, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Principe, J.C., Hsu, H.H., Kuo, J.M.: Analysis of short term memories for neural networks. In: NIPS, pp. 1011–1018 (1993)Google Scholar
  7. 7.
  8. 8.
    Salvucci, D.D., Goldberg, J.H.: Identifying fixations and saccades in eye-tracking protocols. Proceedings of the 2000 Symposium on Eye Tracking Research and Applications, pp. 71–78. ACM Press, New York (2000)Google Scholar
  9. 9.
    Shic, F., Chawarska, K., Scassellati, B.: The incomplete fixation measure. Proceedings of the 2008 Symposium on Eye Tracking Research and Applications, pp. 111–114. ACM Press, New York (2008)CrossRefGoogle Scholar
  10. 10.
    Spakov, O., Miniotas, D.: Application of clustering algorithms in eye gaze visualizations. Inf. Technol. Control 36, 213–216 (2007)Google Scholar
  11. 11.
    Urruty, T., Lew, S., Ihadaddene, N., Simovici, D.A.: Detecting eye fixations by projection clustering. ACM Trans. Multimedia Comput. Commun. Appl. 3, 23:1–23:20 (2007)CrossRefGoogle Scholar
  12. 12.
    Yates, R., Goodman, D.: Probability and stochastic processes: A friendly introduction for electrical & computer engineers. John Wiley, New York (1999)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sudarat Tangnimitchok
    • 1
    Email author
  • Nonnarit O-larnnithipong
    • 1
  • Armando Barreto
    • 1
  • Francisco R. Ortega
    • 2
  • Naphtali D. Rishe
    • 2
  1. 1.Electrical and Computer Engineering DepartmentFlorida International UniversityMiamiUSA
  2. 2.School of Computer and Information SciencesFlorida International UniversityMiamiUSA

Personalised recommendations