Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera
|Hari Singh1 , Jaswinder Singh2|
1 Research Scholar, IKG Punjab Technical University, Kapurthala, India.
2 Associate Professor, Beant College of Engineering and Technology, Gurdaspur, India.
|Correspondence should be addressed to: email@example.com.|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 35-40, Dec-2017
Online published on Dec 31, 2017
Copyright © Hari Singh, Jaswinder Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|View this paper at Google Scholar | DPI Digital Library|
|XML View||PDF Download|
IEEE Style Citation: Hari Singh, Jaswinder Singh, “Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.35-40, 2017.
MLA Style Citation: Hari Singh, Jaswinder Singh "Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera." International Journal of Computer Sciences and Engineering 5.12 (2017): 35-40.
APA Style Citation: Hari Singh, Jaswinder Singh, (2017). Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera. International Journal of Computer Sciences and Engineering, 5(12), 35-40.
|181||183 downloads||101 downloads|
|This paper presents the performance analysis of a blink detector, which detects eye blink, right wink and left wink, under natural & controlled lighting conditions and for variable user distance from the camera. The blink detector has been implemented by using a webcam, a computer and MATLAB software with image processing and computer vision toolbox. It divides the whole process of blink detection into three parts: face and eyes pair localization, blink detection using pixels’ motion analysis and classification of blinks as left wink, right wink and eye blink i.e. blinking both eyes simultaneously. The detection accuracy of the detector was measured under natural and controlled lighting conditions for different values of user distance from the camera. Average detection accuracy of the detector under controlled lighting conditions observed to be 96%, 92% and 88% for detection of eye blink, left wink and right wink, respectively. From the overall analysis it has been observed that the system gives significantly better performance under controlled lighting conditions than under natural lighting conditions, and when the user sits at a distance of about 0.5 meter from the camera.|
|Key-Words / Index Term :|
|real-time eye blink detection, pixels’ motion analysis, varying lighting conditions, distance of user from camera, human-computer interaction|
 E. Missimer and M. Betke, “Blink and Wink Detection for Mouse Pointer Control,” in PETRA’10 Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments, 2010.
 T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, “Drowsy Driver Detection System using Eye Blink Patterns,” in International Conference on Machine and Web Intelligence, 2010, pp. 230–233.
 E. Miluzzo, T. Wang, and A. T. Campbell, “EyePhone : Activating Mobile Phones With Your Eyes,” in Proceedings of the 2nd ACM SIGCOMM Workshop on Netwroking, Systems and Applications on Mobile Handhelds, 2010, pp. 15–20.
 K. Grauman, M. Betke, J. Lombardi, J. Gips, and G. R. Bradski, “Communication via Eye Blinks and Eyebrow Raises : Video-based Human-Computer Interfaces,” Universal Access in the Information Society, vol. 2, no. 4, pp. 359–373, 2003.
 M. Hashimoto, K. Takahashi, and M. Shimada, “Wheelchair Control Using an EOG- and EMG-Based Gesture Interface,” in IEEE/ASME International Conference on Advanced Intelligent Machatronics, 2009, pp. 1212–1217.
 S. S. Deepika and G. Murugesan, “A Novel Approach for Human Computer Interface on Eye Movements for Disabled People,” in 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT 2015), 2015.
 Y. Chen and W. S. Newman, “A Human-Robot Interface Based on Electrooculography,” in Proceedings of the 2004 IEEE International Conference on Robotics and Automation, 2004, pp. 243–248.
 T. Pallejà, E. Rubión, M. Tresanchez, and A. Fernández, “Using the Optical Flow to Implement a Relative Virtual Mouse Controlled by Head Movements,” Journal of Universal Computer Science, vol. 14, no. 19, pp. 3127–3141, 2008.
 T. Rajpathak, R. Kumar, and E. Schwartz, “Eye Detection Using Morphological and Color Image Processing,” in 2009 Florida Conference on Recent Advances in Robotics, FCRAR 2009, pp. 1–6.
 H. Drewes and A. Schmidt, “Interacting with the Computer using Gaze Gestures,” in INTERACT’07 Proceedings of the 11th IFIP TC 13 International Conference on Human Computer Interaction (Part-II), 2007, pp. 475–488.
 A. Krolak and P. Strumillo, “Eye-Blink Detection System for Human-Computer Interaction,” Universal Access in the Information Society, vol. 11, no. 4, pp. 409–419, 2012.
 P. Wang, M. B. Green, and Q. Ji, “Automatic Eye Detection and Its Validation,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
 A. A. Mohammed and S. A. Anwer, “Efficient Eye Blink Detection Method for Disabled- Helping Domain,” International Journal of Advanced Computer Science and Applications, vol. 5, no. 5, pp. 202–206, 2014.