Embedded System for Eye Blink Detection Using Machine Learning Technique. Ibrahim, B., R., Khalifa, F., M., Zeebaree, S., R., Othman, N., A., Alkhayyat, A., Zebari, R., R., & Sadeeq, M., A. In 1st Babylon International Conference on Information Technology and Science 2021, BICITS 2021, pages 58-62, 2021. Institute of Electrical and Electronics Engineers Inc..
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Paper doi abstract bibtex
Paper doi abstract bibtex Nowadays, eye tracking and blink detection are increasingly popular among researchers and have the potential to become a more important component of future perceptual user interfaces. The real-time eye-tracking system has been a fundamental and challenging problem for machine learning problems. The main purpose of this paper is to propose a new method to design an embedded eye blink detection system that can be used for various applications with the lowest cost. This study presents an efficient technique to determine the level of eyes that are closed and opened. We offered a real-time blink detection method by using machine learning and computer vision libraries. The proposed method consists of four phases: (1) taking frame by employing a raspberry pi camera that slotted to the raspberry pi 3 platform, (2) utilizing haar cascade algorithm to identify faces in the captured frames, (3) find facial landmarks by utilizing facial landmark detector algorithm, (4) detect the eyes' region and calculate the eye aspect ratio. The proposed method obtained a high accuracy to indicate eye closing or opening. In this study, an aspect ratio method was used to implement a robust and low-cost embedded eye blink detection system on the raspberry pi platform. This method is exact resourceful, fast, and easy to perform eye blink detection.
@inproceedings{
title = {Embedded System for Eye Blink Detection Using Machine Learning Technique},
type = {inproceedings},
year = {2021},
keywords = {Embedded system,Eye aspect,Eye blink detection,Face detection,Facial landmark detector,Machine learning,Raspberry pi 3},
pages = {58-62},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
id = {4e9e8810-e772-3658-b15b-b7a14875fe93},
created = {2022-09-29T07:48:01.209Z},
file_attached = {true},
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group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
last_modified = {2023-01-12T10:14:06.937Z},
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starred = {false},
authored = {false},
confirmed = {false},
hidden = {false},
notes = {The "Machine Learning" part from this paper is basically just by using a library called "dlib" [8]-[10].<br/><br/>Did not bring anything new, or relevant.},
folder_uuids = {9f2f513d-a81c-4e69-a1c5-34e938a643c8,6aec054e-977e-4d9f-8f01-b49e735da52b},
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abstract = {Nowadays, eye tracking and blink detection are increasingly popular among researchers and have the potential to become a more important component of future perceptual user interfaces. The real-time eye-tracking system has been a fundamental and challenging problem for machine learning problems. The main purpose of this paper is to propose a new method to design an embedded eye blink detection system that can be used for various applications with the lowest cost. This study presents an efficient technique to determine the level of eyes that are closed and opened. We offered a real-time blink detection method by using machine learning and computer vision libraries. The proposed method consists of four phases: (1) taking frame by employing a raspberry pi camera that slotted to the raspberry pi 3 platform, (2) utilizing haar cascade algorithm to identify faces in the captured frames, (3) find facial landmarks by utilizing facial landmark detector algorithm, (4) detect the eyes' region and calculate the eye aspect ratio. The proposed method obtained a high accuracy to indicate eye closing or opening. In this study, an aspect ratio method was used to implement a robust and low-cost embedded eye blink detection system on the raspberry pi platform. This method is exact resourceful, fast, and easy to perform eye blink detection.},
bibtype = {inproceedings},
author = {Ibrahim, Bishar R. and Khalifa, Farhad M. and Zeebaree, Subhi R.M. and Othman, Nashwan A. and Alkhayyat, Ahmed and Zebari, Rizgar R. and Sadeeq, Mohammed A.M.},
doi = {10.1109/BICITS51482.2021.9509908},
booktitle = {1st Babylon International Conference on Information Technology and Science 2021, BICITS 2021}
}Downloads: 0
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