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Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera
Open Access   Article

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: harisdhillon@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 35-40, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.3540

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.
 
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Citation

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.
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Abstract :
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
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