Version 1
: Received: 1 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (11:59:00 CEST)
How to cite:
Francisco Santos, D. Real-time Eye Blink Detection using Computer Vision and Machine Learning. Preprints2024, 2024100131. https://doi.org/10.20944/preprints202410.0131.v1
Francisco Santos, D. Real-time Eye Blink Detection using Computer Vision and Machine Learning. Preprints 2024, 2024100131. https://doi.org/10.20944/preprints202410.0131.v1
Francisco Santos, D. Real-time Eye Blink Detection using Computer Vision and Machine Learning. Preprints2024, 2024100131. https://doi.org/10.20944/preprints202410.0131.v1
APA Style
Francisco Santos, D. (2024). Real-time Eye Blink Detection using Computer Vision and Machine Learning. Preprints. https://doi.org/10.20944/preprints202410.0131.v1
Chicago/Turabian Style
Francisco Santos, D. 2024 "Real-time Eye Blink Detection using Computer Vision and Machine Learning" Preprints. https://doi.org/10.20944/preprints202410.0131.v1
Abstract
This paper presents a real-time eye blink detection system using computer vision techniques and machine learning. The system uti- lizes the MediaPipe face mesh model for facial landmark detection and calculates the Eye Aspect Ratio (EAR) to determine the eye state. Results demonstrate high accuracy and responsiveness, indicating po- tential applications in driver drowsiness detection, human-computer interaction, and medical diagnostics.
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.
