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Gaze Estimation Using Regression Analysis and AAMs Parameters Selected Based on Information Criterion

  • Manabu Takatani
  • Yasuo Ariki
  • Tetsuya Takiguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

One of the most crucial techniques associated with Computer Vision is technology that deals with the automatic estimation of gaze orientation. In this paper, a method is proposed to estimate horizontal gaze orientation from a monocular camera image using the parameters of Active Appearance Models (AAM) selected based on several model selection methods. The proposed method can estimate horizontal gaze orientation more precisely than the conventional method (Ishikawa’s method) because of the following two unique points: simultaneous estimation of horizontal head pose and gaze orientation, and the most suitable model formula for regression selected based on each model selection method. The validity of the proposed method was confirmed by experimental results.

Keywords

Akaike Information Criterion Feature Point Parameter Vector Training Image Minimum Description Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Miyahara, M., Aoki, M., Takiguchi, T., Ariki, Y.: Tagging Video Contents with Positive/Negative Interest. In: The 14th International Multimedia Modeling Conference, pp. 210–219 (2008)Google Scholar
  2. 2.
    Pang, D., Kimura, A., Takeuchi, T., Yamato, J.: A Stochastic Model Of Selective Visual Attention With A Dynamic Bayesian Network. In: IEEE International Conference on Multimedia and Expo., pp. 1073–1076 (2008)Google Scholar
  3. 3.
    Ohno, T., Mukawa, N., Yoshikawa, A.: FreeGaze: gaze tracking systems for everyday gaze interaction. In: Proceedings of the symposium on Eye tracking research & applications, pp. 125–132 (2002)Google Scholar
  4. 4.
    Lucas, B., Kanade, T.: An interactive image registration technique with an application to stereo vision. In: Proc Int’l Joint Conference on Atrificial Intelligence, pp. 674–679 (2005)Google Scholar
  5. 5.
    Yamazoe, H., Utsumi, A., Yonezawa, T., Abe, S.: Remote Gaze Estimation with a Single Camera Based on Facial-Feature Tracking without Special Calibration Actions. In: Proceedings of the symposium on Eye Tracking Research & Applications Symposium, pp. 245–250 (2008)Google Scholar
  6. 6.
    Ishikawa, T., Baker, S., Matthews, I., Kanade, T.: Passive Driver Gaze tracking with Active Appearance Models. In: Proc. 11th World Congress in Intelligent Transport Systems (2004)Google Scholar
  7. 7.
    Viola, P., Jones, M.J.: Robust Real-Time Face Detection. In: International Journal of Computer Vision, vol. 2, pp. 137–154 (2004)Google Scholar
  8. 8.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: European Conference on Computer Vision, pp. 484–498 (1998)Google Scholar
  9. 9.
    Cootes, T.F., Walker, K., Taylor, C.J.: view-based Acitve Appearance Models. In: Forth IEEE Conference on Automatic Face and Gesture Recognition, pp. 227–232 (2000)Google Scholar
  10. 10.
    Hirotugu, A.: A new look at the statistical model identification. In: IEEE Transactions on Automatic Control, vol. 19, pp. 716–723 (1974)Google Scholar
  11. 11.
    Rissanen, J.: Infomation and Complexity in Statistical Modeling. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  12. 12.
    McQuarrie, A.D.R., Tsai, C.L.: Regression and Time Series Model Selection. World Scientific, Singapore (1998)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manabu Takatani
    • 1
  • Yasuo Ariki
    • 2
  • Tetsuya Takiguchi
    • 2
  1. 1.Graduate School of EngineeringKobe UniversityJapan
  2. 2.Organization of Advanced Science and TechnologyKobe UniversityJapan

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