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Eye State Recognizer Using Light-Weight Architecture for Drowsiness Warning

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Abstract
The eye are a very important organ in the human body. The eye area and eyes contain lots of useful information about human interaction with the environment. Many studies have relied on eye region analyzes to build the medical care, surveillance, interaction, security, and warning systems. This paper focuses on extracting eye region features to detect eye state using the light-weight convolutional neural networks with two stages: eye detection and classification. This method can apply on simple drowsiness warning system and perform well on Intel Core I7-4770 CPU @ 3.40 GHz (Personal Computer - PC) and on quad-core ARM Cortex-A57 CPU (Jetson Nano device) with 19.04 FPS and 17.20 FPS (frames per second), respectively.
Author(s)
웬 주이 린푸트로 무하마드 드위스난토조강현
Issued Date
2021
Type
Article
Keyword
Convolutional neural network (CNN)Deep learningDrowsiness warningEye classificationEye detectionEye state recognizer
DOI
10.1007/978-3-030-73280-6_41
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9136
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_3_030_73280_6_41&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,e%20State%20Recognizer%20Using%20Light-Weight%20Architecture%20for%20Drowsiness%20Warning&offset=0&pcAvailability=true
Publisher
Lecture Notes in Computer Science
Location
독일
Language
영어
ISSN
0302-9743
Citation Volume
12672
Citation Number
1
Citation Start Page
518
Citation End Page
530
Appears in Collections:
Engineering > IT Convergence
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