Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Gaze Direction Estimation Method Using Support Vector Machines (SVMs)

Support Vector Machines을 이용한 시선 방향 추정방법

  • 유정 ((주)ATM) ;
  • 우경행 (울산대학교 전기전자정보시스템학부) ;
  • 최원호 (울산대학교 전기전자정보시스템학부)
  • Published : 2009.04.01
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Abstract

A human gaze detection and tracing method is importantly required for HMI(Human-Machine-Interface) like a Human-Serving robot. This paper proposed a novel three-dimension (3D) human gaze estimation method by using a face recognition, an orientation estimation and SVMs (Support Vector Machines). 2,400 images with the pan orientation range of $-90^{\circ}{\sim}90^{\circ}$ and tilt range of $-40^{\circ}{\sim}70^{\circ}$ with intervals unit of $10^{\circ}$ were used. A stereo camera was used to obtain the global coordinate of the center point between eyes and Gabor filter banks of horizontal and vertical orientation with 4 scales were used to extract the facial features. The experiment result shows that the error rate of proposed method is much improved than Liddell's.

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References

  1. S. W. Shih and J. Lin, 'A novel approach to 3-D gaze tracking using stereo cameras,' IEEE Trans. on System, Man, and Cybernetics-Part B: Cybernetics, vol. 34, no. 1, pp. 234-245, Feb. 2004 https://doi.org/10.1109/TSMCB.2003.811128
  2. Z. Zhn, and Q. Ji, 'Novel eye gaze tracking techniques under natural head movement,' IEEE Trans. on Biomedical Engineering, vol. 54, no. 12, pp. 2246-2260, Dec. 2007 https://doi.org/10.1109/TBME.2007.895750
  3. R. Atienza and A. Zelinsky, 'Intuitive human-robot interaction through active 3D gaze tracking,' Robotics Research, STAR 15, pp. 172-181,2005 https://doi.org/10.1007/b97958
  4. F. Wallhoff, M. Abla$\beta$meier, and G. Rigoll, 'Multimodal face detection, head orientation and eye gaze tracking,' in Proc. of IEEE Conf. on Multisensor Fusion Integration for Intelligent Systems, Heidelberg, German, pp. 13-18, Sep. 2006
  5. R. Rae and H. J. Ritter, 'Recognition of human head orientation based on artificial neural networks,' IEEE Trans. on Neural Networks, vol. 9, no. 2, Mar. 1998
  6. L. Zhao, G. Pingali, and I. Carlbom, 'Real-time head orientation estimation using neural networks,' in Proc. of IEEE Can! on Image Processing, pp. I-297-I-300, 2002
  7. Y. Li, S. Gong, and H. Liddell, 'Support vector regression and classification based multi-view face detection and recognition,' in Proc. of IEEE Conf on Automatic Face and Gesture Recognition, pp. 300-305, Mar. 2000
  8. S. M. Mohsin, M. Y. Javed, and A. Anjum, 'Face recognition using bank of Cabor filters,' in Proc. of IEEE Conf. on Emerging Technologies, Peshawar, Pakistan, pp. 144-150, Nov. 2006
  9. Y. W. Chen and K. Kubo, 'A robust eye detection and tracking techniques using Gabor filters,' in Proc. of IEEE Conf. on Intelligent Information Hiding and Multimedia Signal Processing, pp. 109-112, Nov. 2007
  10. M. Soriano, B. Martinkauppi, S. Huovinen, and M. Laaksonen, 'Skin detection in video under changing illumination conditions,' in Proc. of IEEE Conf. on Pattern Recognition, vol, 1, pp. 839-842, Sep. 2000 https://doi.org/10.1109/ICPR.2000.905542
  11. H. M. Zhang, D. B. Zhao, and W. Gao, 'Face detection under rotation in image plane using skin color model, neural network and feature-based face model,' Computer Journal, vol. 25, no. 11, pp. 1250-1256, 2002
  12. J. R. Movellan, 'Tutorial on Gabor filters,' http://mplab.ucsd.edulwordpress/tutorials/gabor.pdf
  13. E. Osuna, R. Freund, and R. Girosi, 'An improved training algorithm for support vector machines,' in Proc. of IEEE Workshop on Neural Networks for Signal Processing, pp. 276-285, Sep. 1997 https://doi.org/10.1109/NNSP.1997.622408

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