Skip to main content
EURASIP Journal on Image and Video Processing
Download PDF
  • Research Article
  • Open access
  • Published:

Automatic Eye Winks Interpretation System for Human-Machine Interface

EURASIP Journal on Image and Video Processing volume 2007, Article number: 065184 (2007) Cite this article

Abstract

This paper proposes an automatic eye-wink interpretation system for human-machine interface to benefit the severely handicapped people. Our system consists of (1) applying the support vector machine (SVM) to detect the eyes, (2) using the template matching algorithm to track the eyes, (3) using SVM classifier to verify the open or closed eyes and convert the eye winks into a sequence of codes (0 or 1), and (4) applying the dynamic programming to translate the code sequence to a certain valid command. Different from the previous eye-gaze tracking methods, our system identifies the open or closed eye, and then interprets the eye winking as certain commands for human-machine interface. In the experiments, our system demonstrates better performance as well as higher accuracy.

[12345678910111213141516171819]

References

  1. Hallinan PW: Recognizing human eyes. Geometric Methods in Computer Vision, July 1991, San Diego, Calif, USA, Proceedings of SPIE 1570: 214-226.

    Article  Google Scholar 

  2. Amarnag S, Kumaran RS, Gowdy JN: Real time eye tracking for human computer interfaces. Proceedings of the International Conference on Multimedia and Expo (ICME '03), July 2003, Baltimore, Md, USA 3: 557-560.

    Google Scholar 

  3. Ji Q, Yang X: Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging 2002,8(5):357-377. 10.1006/rtim.2002.0279

    Article  MathSciNet  MATH  Google Scholar 

  4. Smith P, Shah M, da Vitoria Lobo N: Monitoring head/eye motion for driver alertness with one camera. Proceedings of the 15th International Conference on Pattern Recognition (ICPR '00), September 2000, Barcelona, Spain 4: 636-642.

    Article  Google Scholar 

  5. D'Orazio T, Leo M, Spagnolo P, Guaragnella C: A neural system for eye detection in a driver vigilance application. Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems (ITS '04), October 2004, Washington, DC, USA 320-325.

    Google Scholar 

  6. Van Orden KF, Jung T-P, Makeig S: Combined eye activity measures accurately estimate changes in sustained visual task performance. Biological Psychology 2000,52(3):221-240. 10.1016/S0301-0511(99)00043-5

    Article  Google Scholar 

  7. Shaw R, Crisman E, Loomis A, Laszewski Z: The eye wink control interface: using the computer to provide the severely disabled with increased flexibility and comfort. Proceedings of the 3rd Annual IEEE Symposium on Computer-Based Medical Systems (CBMS '90), June 1990, Chapel Hill, NC, USA 105-111.

    Google Scholar 

  8. Gan L, Cui B, Wang W: Driver fatigue detection based on eye tracking. Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA '06), June 2006, Dalian, China 2: 5341-5344.

    Google Scholar 

  9. Haro A, Flickner , Essa I: Detecting and tracking eyes by using their physiological properties, dynamics, and appearance. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), June 2000, Hilton Head Island, SC, USA 1: 163-168.

    Article  Google Scholar 

  10. Iijima A, Haida M, Ishikawa N, Minamitani H, Shinohara Y: Head mounted goggle system with liquid crystal display for evaluation of eye tracking functions on neurological disease patients. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '03), September 2003, Cancun, Mexico 4: 3225-3228.

    Google Scholar 

  11. Fasel I, Fortenberry B, Movellan J: A generative framework for real time object detection and classification. Computer Vision and Image Understanding 2005,98(1):182-210. 10.1016/j.cviu.2004.07.014

    Article  Google Scholar 

  12. Zhu Z, Ji Q: Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Computer Vision and Image Understanding 2005,98(1):124-154. 10.1016/j.cviu.2004.07.012

    Article  Google Scholar 

  13. Morimoto CH, Flickner M: Real-time multiple face detection using active illumination. Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG '00), March 2000, Grenoble, France 8-13.

    Google Scholar 

  14. Liu X, Xu F, Fujimura K: Real-time eye detection and tracking for driver observation under various light conditions. Proceedings of the IEEE Intelligent Vehicle Symposium (IV '02), June 2002, Versailles, France 2: 344-351.

    Google Scholar 

  15. Hansen DW, Pece AEC: Eye tracking in the wild. Computer Vision and Image Understanding 2005,98(1):155-181. 10.1016/j.cviu.2004.07.013

    Article  Google Scholar 

  16. Chai D, Ngan KN: Face segmentation using skin-color map in videophone applications. IEEE Transactions on Circuits and Systems for Video Technology 1999,9(4):551-564. 10.1109/76.767122

    Article  Google Scholar 

  17. Chai D, Phung SL, Bouzerdoum A: Skin color detection for face localization in human-machine communications. Proceedings of the 6th International, Symposium on Signal Processing and Its Applications (ISSPA '01), August 2001, Kuala Lumpur, Malaysia 1: 343-346.

    Article  Google Scholar 

  18. Vapnik VN: The Nature of Statistical Learning Theory. Springer, New York, NY, USA; 1995.

    Book  MATH  Google Scholar 

  19. Otsu N: A threshold selection method from gray-level histograms. IEEE Transcations on Systems, Man, and Cybernetics 1979,9(1):62-66. 10.1109/TSMC.1979.4310076

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan

    Che Wei-Gang & Chung-Lin Huang

  2. Department of Informatics, Fo-Guang University, I-Lan, Taiwan

    Chung-Lin Huang

  3. Institute of Information Science, Academic Sinica, Taipei, Taiwan

    Wen-Liang Hwang

Authors
  1. Che Wei-Gang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chung-Lin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-Liang Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Che Wei-Gang.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Wei-Gang, C., Huang, CL. & Hwang, WL. Automatic Eye Winks Interpretation System for Human-Machine Interface. J Image Video Proc 2007, 065184 (2007). https://doi.org/10.1155/2007/65184

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/65184

Keywords