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.
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References
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)
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)
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)
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)
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)
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)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. In: International Journal of Computer Vision, vol. 2, pp. 137–154 (2004)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: European Conference on Computer Vision, pp. 484–498 (1998)
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)
Hirotugu, A.: A new look at the statistical model identification. In: IEEE Transactions on Automatic Control, vol. 19, pp. 716–723 (1974)
Rissanen, J.: Infomation and Complexity in Statistical Modeling. Springer, Heidelberg (2007)
McQuarrie, A.D.R., Tsai, C.L.: Regression and Time Series Model Selection. World Scientific, Singapore (1998)
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Takatani, M., Ariki, Y., Takiguchi, T. (2011). Gaze Estimation Using Regression Analysis and AAMs Parameters Selected Based on Information Criterion. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22822-3_40
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DOI: https://doi.org/10.1007/978-3-642-22822-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22821-6
Online ISBN: 978-3-642-22822-3
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