A fast and adaptive video-based method for eye blink rate estimation
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Authors
Mohammadi, Gheis
Shanbehzadeh, Jamshid
Sarrafzadeh, Hossein
Shanbehzadeh, Jamshid
Sarrafzadeh, Hossein
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Grantor
Date
2015-06-16
Supervisors
Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
eye blink rate (EBR)
video-based
machine vision
human-computer interaction
low resolution camera
adaptive method
algorithms
video-based
machine vision
human-computer interaction
low resolution camera
adaptive method
algorithms
ANZSRC Field of Research Code (2020)
Citation
Mohammadi, G., Shanbehzadeh, J., & Sarrafzadeh, A. (2015). A Fast and Adaptive Video-Based Method for Eye Blink Rate Estimation. International Journal of Advanced Computer Research, 5 (19), pp.105-114.
Abstract
Eye blink rate (EBR) estimation is one of the informative cues and challengeable areas in eye-based systems that has a wide range of applications like the detecting a driver’s drowsiness, anxiety analysis, diseases detection and etc.
This paper presents an adaptive blink rate estimation algorithm. The advantages of this algorithm are simplicity, accuracy, fastness, low computational cost and robustness against lighting conditions. This algorithm is based on simple image processing techniques. The first step of blink detection method is eye detection. To accomplish this task, we suppose that a fairly large face image is available. Each frame of the input video is processed and the location of the eye is found. The next step calculates a value to determine the state of eye. Our method uses this value to EBR estimation.
This paper presents the accuracy of new algorithm by providing a data set of several people and comparing the results with some of the strong relevant methods. The experimental results show that the proposed method has overall accuracy of 98.91%. The average blink rate estimation time of new algorithm for a sample is less than 80 microseconds, which makes it suitable for real-time applications.
Publisher
ACCENT Society (India)
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©2015 ACCENTS
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Attribution-NonCommercial-NoDerivs 3.0 New Zealand