Abstract
The purpose of this study is to develop an appearance-based method for estimating gaze directions from low resolution images. The problem of estimating directions using low resolution images is that the position of an eye region cannot be determined accurately. In this work, we introduce two key ideas to cope with the problem: incorporating training images of eye regions with artificially added positioning errors, and separating the factor of gaze variation from that of positioning error based on N-mode SVD (Singular Value Decomposition). We show that estimation of gaze direction in this framework is formulated as a bilinear problem that is then solved by alternatively minimizing a bilinear cost function with respect to gaze direction and position of the eye region. In this paper, we describe the details of our proposed method and show experimental results that demonstrate the merits of our method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baluja, S., Pomerleau, D.: Non-intrusive gaze tracking using artificial neural networks. In: Advances in Neural Information Processing Systems, pp. 753–760 (1993)
Beymer, D., Flickner, M.: Eye gaze tracking using an active stereo head. In: Proc. IEEE CVPR, pp. 451–458 (2003)
Cristinacce, D., Cootes, T.: Facial feature detection using adaboost with shape constraints. In: Proc. British Machine Vision Conference, pp. 231–240 (2003)
Hutchinson, T., White Jr, K., Martin, W., Reichert, K., Frey, L.: Human-computer interaction using eye-gaze input. IEEE Trans. on Systems, Man, and Cybernetics 19(6), 1527–1534 (1989)
Ishikawa, T., Baker, S., Matthews, I., Kanade, T.: Passive driver gaze tracking with active appearance models. In: Proc. Intelligent Transportation Systems (October 2004)
Matsumoto, Y., Zelinsky, A.: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. In: Proc. IEEE FG, pp. 499–505 (2000)
Ohno, T., Mukawa, N.: A free-head, simple calibration, gaze tracking system that enables gaze-based interaction. In: Proc. Eye Tracking Research and Application symposium, pp. 115–122 (2004)
Oka, K., Sato, Y., Nakanishi, Y., Koike, H.: Head pose estimation system based on particle filtering with adaptive diffusion control. In: IAPR Conf. Machine Vision Applications (MVA 2005), May 2005, pp. 586–589 (2005)
Shum, H.-Y., Ikeuchi, K., Reddy, R.: Principal component analysis with missing data and its application to polyhedral object modeling. IEEE Trans. PAMI 17(9), 854–867 (1995)
Stiefelhagen, R., Yang, J., Waibel, A.: Tracking eyes and monitoring eye gaze. In: Proc. Workshop on Perceptual User Interfaces, Banff, Canada (October 1997)
Tan, K.-H., Kriegman, D., Ahuja, N.: Appearance-based eye gaze estimation. In: Proc. IEEE Workshop on Applications of Computer Vision, pp. 191–195 (2002)
Vasilescu, M.A.O., Terzopoulos, D.: Multilinear image analysis for facial recognition. In: Proc. ICPR, pp. 511–514 (2002)
Vasilescu, M.A.O., Terzopoulos, D.: Multilinear independent components analysis. In: Proc. IEEE CVPR, pp. 547–553 (2005)
Wang, J.-G., Sung, E., Venkateswarlu, R.: Eye gaze estimation from a single image of one eye. In: Proc. IEEE ICCV, pp. 136–143 (2003)
Xu, L.-Q., Machin, D., Sheppard, P.: A novel approach to real-time non-intrusive gaze finding. In: Proc. British Machine Vision Conference (1998)
Yao, T., Li, H., Liu, G., Ye, X., Gu, W., Jin, Y.: A fast and robust face location and feature extraction system. In: Proc. IEEE ICIP, pp. 157–160 (2002)
Yoo, D., Chung, M.: Non-intrusive eye gaze estimation without knowledge of eye pose. In: Proc. IEEE FG, pp. 785–790 (2004)
Zhu, Z., Ji, Q.: Eye gaze tracking under natural head movements. In: Proc. IEEE CVPR, pp. 918–923 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ono, Y., Okabe, T., Sato, Y. (2006). Gaze Estimation from Low Resolution Images. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_18
Download citation
DOI: https://doi.org/10.1007/11949534_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
eBook Packages: Computer ScienceComputer Science (R0)