Fernando, B.A., Sridhar, A., Talebi, S., Waczak, J., & Lary, D.J. (2022). Unsupervised Blink Detection Using Eye Aspect Ratio Values. Preprints. https://doi.org/10.20944/preprints202203.0200.v1
Chicago/Turabian Style
Fernando, B.A., John Waczak and David J. Lary. 2022 "Unsupervised Blink Detection Using Eye Aspect Ratio Values" Preprints. https://doi.org/10.20944/preprints202203.0200.v1
Abstract
The eyes serve as a window into underlying physical and cognitive processes. Although factors such as pupil size have been studied extensively, a less explored yet potentially informative aspect is blinking. Given its novelty, blink detection techniques are far less available compared to eye-tracking and pupil size estimation tools. In this work, we present a new unsupervised machine learning blink detection strategy using existing eye-tracking technology. The method is compared to two existing techniques. All three algorithms make use of eye aspect ratio values for blink detection. Accurate and rapid blink detection complements existing eye-tracking research and may provide a new informative index of physical and mental status.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
