Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Unsupervised Blink Detection Using Eye Aspect Ratio Values

Version 1 : Received: 8 March 2022 / Approved: 15 March 2022 / Online: 15 March 2022 (10:31:40 CET)

How to cite: Fernando, B.A.; Sridhar, A.; Talebi, S.; Waczak, J.; Lary, D.J. Unsupervised Blink Detection Using Eye Aspect Ratio Values. Preprints 2022, 2022030200 (doi: 10.20944/preprints202203.0200.v1). Fernando, B.A.; Sridhar, A.; Talebi, S.; Waczak, J.; Lary, D.J. Unsupervised Blink Detection Using Eye Aspect Ratio Values. Preprints 2022, 2022030200 (doi: 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.

Supplementary and Associated Material

Keywords

Machine Learning; Eye Tracking; Blink Detection

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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