OMEG: Oulu Multi-Pose Eye Gaze Dataset

  • Qiuhai He
  • Xiaopeng Hong
  • Xiujuan Chai
  • Jukka Holappa
  • Guoying Zhao
  • Xilin Chen
  • Matti Pietikäinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)


Data is in a very important position for pattern recognition tasks including eye gaze estimation. In the literature, most researchers used normal face datasets, which are not specifically designed for eye gaze estimation. As a result, it is difficult to obtain fine labeled eye gaze direction. Therefore large datasets with well-defined gaze directions are desired.

To facilitate related researches, we collect and establish the Oulu Multi-pose Eye Gaze Dataset. Inspired by the psychological observation that gaze direction is intrinsically linked with the head orientation, we are devoted to a new data set of eye gaze images captured under multiple head poses. It finally results in a dataset containing over 40K images from 50 subjects, who were asked to fixate on 10 special points on screen under different poses respectively. We investigate a new eye gaze estimation approach by using the IGO based description, and compare it with other popular eye gaze estimation approaches to provide the baseline results on our dataset.


Eye gaze Head pose Dataset 


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  1. 1.
    Fitch, A., Kadyrov, A., Christmas, W., Kittler, J.: Orientation correlation. In: Proc. BMVC. Citeseer, pp. 1–10 (2002)Google Scholar
  2. 2.
    Smith, B., Yin, Q., Nayar, S.: Gaze locking: passive eye contact detection for human-object interaction. In: Proc. UIST., pp. 271–280. ACM (2013)Google Scholar
  3. 3.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intelligent Systems and Technology, 2(27) (2011)Google Scholar
  4. 4.
    Morimoto, C., Amir, A., Flickner, M.: Detecting eye position and gaze from a single camera and 2 light sources. In: Proc. ICPR., pp. 314–317. IEEE (2002)Google Scholar
  5. 5.
    Beymer, D., Flickner, M.: Eye gaze tracking using an active stereo head. In: Proc. CVPR. IEEE (2003)Google Scholar
  6. 6.
    Hansen, D., Pece, A.: Eye tracking in the wild. Comput. Vis. Image Und. 98(1), 155–181 (2005)CrossRefGoogle Scholar
  7. 7.
    Hansen, D., Ji, Q.: In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Lu, F., Okabe, T., Sugano, Y., Sato, Y.: Learning gaze biases with head motion for head pose-free gaze estimation. Image and Vision Computing 32(3), 169–179 (2014)CrossRefGoogle Scholar
  10. 10.
    Lu, F., Sugano, Y., Okabe, T., Sato, Y.: Adaptive Linear Regression for Appearance-Based Gaze Estimation. IEEE Trans. Pattern Anal. Mach. Intell., PrePrints (2014)Google Scholar
  11. 11.
    Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Subspace learning from image gradient orientations. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2454–2466 (2012)CrossRefGoogle Scholar
  12. 12.
    Tzimiropoulos, G., Argyriou, V., Zafeiriou, S., Stathaki, T.: Robust fft-based scale-invariant image registration with image gradients. IEEE Trans Pattern Anal. Mach. Intell. 32(10), 1899–1906 (2010)CrossRefGoogle Scholar
  13. 13.
    Bar-Itzhack, I.: New method for extracting the quaternion from a rotation matrix. Journal of Guidance, Control, and Dynamics 23(6), 1085–1087 (2000)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Wang, J., Sung, E.: Study on eye gaze estimation. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics 32(3), 332–350 (2002)CrossRefGoogle Scholar
  16. 16.
    Kim, K., Ramakrishna, R.: Vision-based eye-gaze tracking for human computer interface. In: Proc. SMC, pp. 324–329. IEEE (1999)Google Scholar
  17. 17.
    Tan, K., Kriegman, D., Ahuja, N.: Appearance-based eye gaze estimation. In: Proc. WACV, pp. 191–195. IEEE (2002)Google Scholar
  18. 18.
    Symons, L., Lee, K., Cedrone, C., Nishimura, M.: What are you looking at? acuity for triadic eye gaze. J. Gen. Psychol. 131(4), 451–469 (2004)Google Scholar
  19. 19.
    Cline, M.: The perception of where a person is looking. Am. J. Psychol. 80(1), 41–50 (1967)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Gamer, M., Hecht, H.: Are you looking at me? Measuring the cone of gaze. J. Exp. Psychol. [Hum Percept.]. 33(3), 705–715 (2007)CrossRefGoogle Scholar
  21. 21.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  22. 22.
    Duda, R., Hart, P., Stork, D.: Pattern classification. John Willey & Sons 2, 114–124 (2001)Google Scholar
  23. 23.
    Jenkins, R.: The lighter side of gaze perception. Perception 36, 1266–1268 (2007)CrossRefGoogle Scholar
  24. 24.
    Valenti, R., Sebe, N., Gevers, T.: Combining Head Pose and Eye Location Information for Gaze Estimation. IEEE Trans. Image Process 21(2), 802–815 (2012)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Ando, S.: Luminance-induced shift in the apparent direction of gaze. Perception 31, 657–674 (2002)CrossRefGoogle Scholar
  26. 26.
    Asteriadis, S., Soufleros, D., Karpouzis, K., Kollias, S.: A natural head pose and eye gaze dataset. In: Proc. AFFINE Workshop (2009)Google Scholar
  27. 27.
    Baluja, S., Pomerleau, D.: Non-intrusive gaze tracking using artificial neural networks. Tech. rep., Department of Computer Science, Carnegie Mellon University (1994)Google Scholar
  28. 28.
    Langton, S., Honeyman, H., Tessler, E.: The influence of head contour and nose angle on the perception of eye-gaze direction. Perception & Psychophysics 66(5), 752–771 (2004)CrossRefGoogle Scholar
  29. 29.
    Zhang, T., Tang, Y.Y., Fang, B., Shang, Z., Liu, X.: Face recognition under varying illumination using gradientfaces. IEEE Trans. Image Process. 18(11), 2599–2606 (2009)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Weidenbacher, U., Layher, G., Strauss, P., Neumann, H.: A comprehensive head pose and gaze database. In: Proc. IET (2007)Google Scholar
  31. 31.
    Hong, X., Zhao, G., Pietikainen, M.: Pose Estimation via Complex-Frequency Domain Analysis of Image Gradient Orientations. In: Proc. ICPR. IEEE (2014)Google Scholar
  32. 32.
    Zhao, X., Shan, S., Chai, X., Chen, X.: Cascaded Shape Space Pruning for Robust Facial Landmark Detection. In: Proc. ICCV. IEEE (2013)Google Scholar
  33. 33.
    Sugano, Y., Matsushita, Y., Sato, Y.: Appearance-Based Gaze Estimation Using Visual Saliency. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 329–341 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qiuhai He
    • 1
  • Xiaopeng Hong
    • 1
  • Xiujuan Chai
    • 2
  • Jukka Holappa
    • 1
  • Guoying Zhao
    • 1
  • Xilin Chen
    • 1
    • 2
  • Matti Pietikäinen
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of OuluOuluFinland
  2. 2.Key Lab of Intelligent Information Processing of the Chinese Academy of SciencesThe Institute of Computing Technology of the Chinese Academy of SciencesBeijingPeople’s Republic of China

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