Journal of the Ergonomics Society of Korea (대한인간공학회지)

Ergonomics Society of Korea (대한인간공학회)
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Classification between Intentional and Natural Blinks in Infrared Vision Based Eye Tracking System

  • Kim, Song-Yi (Department of Emotion Engineering, Sangmyung University) ;
  • Noh, Sue-Jin (Department of Emotion Engineering, Sangmyung University) ;
  • Kim, Jin-Man (Department of Computer Science, Sangmyung University) ;
  • Whang, Min-Cheol (Department of Emotion Engineering, Sangmyung University) ;
  • Lee, Eui-Chul (Division of Computer Science, Sangmyung University)
  • Received : 2012.07.23
  • Accepted : 2012.07.29
  • Published : 2012.08.31
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Abstract

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

Keywords

References

  1. Agostino, R., Bologna, M., Dinapoli, L., Gregori, B., Fabbrini, G., Accornero, N. and Berardelli, A., "Voluntary, spontaneous, and reflex blinking in parkinson's disease", Movement Disorders, 23(5), 669-675, 2008. https://doi.org/10.1002/mds.21887
  2. Al-Haddad, A., Sudirman, R. and Omar, C., "Gaze at desired destination, and wheelchair will navigate towards it. new technique to guide wheelchair motion based on EOG signals", Informatics and Computational Intelligence(ICI) First International Conference on, 126-131, 2011.
  3. Berman, B. D., Horovitz, S. G., Morel, B. and Hallett, M., "Neural correlates of blink suppression and the buildup of a natural bodily urge", NeuroImage, 59(2), 1441-1450, 2011.
  4. Chen, Y. and Newman, W. S., "A human-robot interface based on electrooculography", Robotics and Automation(ICRA'04) IEEE International Conference on, 243-248, 2004.
  5. Chang, Chih-Chung and Lin, Chih-Jen., LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27: 1-27: 27, 2011.
  6. Deng, L. Y., Hsu, C. L., Lin, T. C., Tuan, J. S. and Chang, S. M., "EOGbased Human-Computer interface system development", Expert Systems with Applications, 37(4), 3337-3343, 2010. https://doi.org/10.1016/j.eswa.2009.10.017
  7. Denney, D. and Denney, C., "The eye blink electro-oculogram", British Journal of Ophthalmology, 68(4), 225-228, 1984. https://doi.org/10.1136/bjo.68.4.225
  8. Kaneko, K. and Sakamoto, K., "Evaluation of three types of blinks with the use of electro-oculogram and electromyogram", Perceptual and Motor Skills, 88(3), 1037-1052, 1999. https://doi.org/10.2466/pms.1999.88.3.1037
  9. Kim, J., Lee, E. C. and Lim, J. S., A new objective visual fatigue measurement system by using a remote infrared camera, Computer Science and Software Engineering(JCSSE 2011) International Joint Conference on, 182-186, 2011.
  10. Krolak, A. and Strumiłło, P., "Eye-blink detection system for humancomputer interaction", Universal Access in the Information Society, 1-11, 2010.
  11. Lee, E. C. and Park, K. R., Measuring Eyestrain from LCD TV According to Adjustment Factors of Image, IEEE Transactions on Consumer Electronics, 55(3), 1447-1452, 2009. https://doi.org/10.1109/TCE.2009.5278012
  12. Lee, E. C., Heo, H. and Park, K. R., The Comparative Measurements of Eyestrain Caused by 2D and 3D Displays, IEEE Transactions on Consumer Electronics, 53(6), 1677-1683, 2010.
  13. Morency, L. P., Latent-Dynamic Discriminative Models for Continuous Gesture Recognition, Computer Vision and Pattern Recognition International Conference on, 1-8, 2007.
  14. Pander, T., Przybyla, T. and Czabanski, R., "An application of detection function for the eye blinking detection", Human System Interactions Conference on, 287-291, 2008.
  15. Pearce, J., "Observations on the blink reflex", European Neurology, 59 (3-4), 221-223, 2008. https://doi.org/10.1159/000114053
  16. Septanto, H., Prihatmanto, A. S. and Indrayanto, A., "A computer cursor controlled by eye movements and voluntary eye winks using a single channel EOG", Electrical Engineering and Informatics(ICEEI'09) International Conference on, 117-120, 2009.
  17. Skotte, J., Nøjgaard, J. K., Jørgensen, L., Christensen, K. and Sjøgaard, G., "Eye blink frequency during different computer tasks quantified by electrooculography", European Journal of Applied Physiology, 99(2), 113-119, 2007.
  18. Suykens, J. A. K. and Vandewalle, J., Least Squares Support Vector Machine Classifiers, Neural Processing Letters, 9(3), 293-300, 1999. https://doi.org/10.1023/A:1018628609742
  19. Usakli, A., Gurkan, S., Aloise, F., Vecchiato, G. and Babiloni, F., "A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases", Engineering in Medicine and Biology Society (EMBC 2009) Annual International Conference of the IEEE, 543-546, 2009.
  20. Yoon, H. W., Chung, J. Y., Song, M. S. and Park, H. W., "Neural correlates of eye blinking; improved by simultaneous fMRI and EOG measurement", Neuroscience Letters, 381(1), 26-30, 2005. https://doi.org/10.1016/j.neulet.2005.01.077