Version 1
: Received: 7 August 2024 / Approved: 8 August 2024 / Online: 8 August 2024 (08:50:55 CEST)
How to cite:
Ata, F.; Ayturan, K.; Hardalaç, F.; Kutbay, U. Deep Learning-Based Eye Blink Detection in Video Frames: Performance Analysis of Various Models. Preprints2024, 2024080597. https://doi.org/10.20944/preprints202408.0597.v1
Ata, F.; Ayturan, K.; Hardalaç, F.; Kutbay, U. Deep Learning-Based Eye Blink Detection in Video Frames: Performance Analysis of Various Models. Preprints 2024, 2024080597. https://doi.org/10.20944/preprints202408.0597.v1
Ata, F.; Ayturan, K.; Hardalaç, F.; Kutbay, U. Deep Learning-Based Eye Blink Detection in Video Frames: Performance Analysis of Various Models. Preprints2024, 2024080597. https://doi.org/10.20944/preprints202408.0597.v1
APA Style
Ata, F., Ayturan, K., Hardalaç, F., & Kutbay, U. (2024). Deep Learning-Based Eye Blink Detection in Video Frames: Performance Analysis of Various Models. Preprints. https://doi.org/10.20944/preprints202408.0597.v1
Chicago/Turabian Style
Ata, F., Fırat Hardalaç and Uğurhan Kutbay. 2024 "Deep Learning-Based Eye Blink Detection in Video Frames: Performance Analysis of Various Models" Preprints. https://doi.org/10.20944/preprints202408.0597.v1
Abstract
The human eye is a vital source of collecting information worldwide via daily observation. The computer science area recognizes it in research as a human-computer interaction. Human blink is an integral part of this observation as a research topic. Several studies have been conducted on eye blinking and left-right eye movements. Previous studies conducted on the eye blink consider the availability of hardware devices high in the budget, high Precision, and low light expo-sure, unlike our study, which uses low-budget devices as simple as a webcam and more versatile usable techniques, incredibly both dim and high light to name a few. This study offers the observational approach via ResNet101v2, VGG-19, and Convolution Neural Network (CNN) architectures. Outperforming both VGG-19 and CNN in this study, ResNet101v2 achieves an impressive accuracy of 98.2%. In contemporary times, deep learning in AI is taking an advanced form. This study attempts to provide a new insight into the real-world implementation of the eye blink.
Keywords
eye blink detection; deep learning; human computer interaction (HCI); image recognition
Subject
Engineering, Electrical and Electronic Engineering
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.
