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Paputungan, Irving V.. Non-invasive automatic drowsiness detection using independently recurrent neural network. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
Non-invasive automatic drowsiness detection using independently recurrent neural network
Name: Arrie Kurniawardhani
Organization :
Universitas Islam Indonesia. Informatics Department
Email :
arrie.kurniawardhani@uii.ac.id
Name: Rahman, Hanif A.
Organization :
Universitas Islam Indonesia. Informatics Department
Email :
hanifarahman@students.uii.ac.id
Name: Paputungan, Irving V.
Organization :
Universitas Islam Indonesia. Informatics Department
Email :
irving@uii.ac.id
LCSH:
Drowsiness
-- Detection
Abstract:
Drowsiness is a critical factor contributing to
various accidents, particularly in traffic and occupational
settings. To mitigate this issue, we propose an algorithm for
automatic drowsiness detection. The algorithm begins with
face detection, followed by the identification of facial
landmarks and the computation of the Eye Aspect Ratio (EAR)
to detect blinks. Blink features are then extracted and
preprocessed, and the sequences are classified to determine
drowsiness. For the classification of these temporal blink
sequences, we utilize the Independently Recurrent Neural
Network (IndRNN), an advanced Deep Learning technique
derived from Recurrent Neural Networks, designed specifically
for temporal data processing. This approach leverages the
IndRNN's ability to effectively handle long-term dependencies
in sequential data. The performance of the algorithm was
evaluated using video data from the University of Texas at
Arlington Real-Life Drowsiness Dataset (UTA-RLDD). The
results demonstrate that the IndRNN achieved a classification
accuracy of 91.88% for temporal blink data.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-06-06
Issued:
2025-06-06
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.534-539). Phuket : Prince of Songkla University
eng
©copyrights King Mongkut's University of Technology North Bangkok
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0.017911 วินาที
Arrie Kurniawardhani
| Title | Contributor | Type |
|---|---|---|
| Non-invasive automatic drowsiness detection using independently recurrent neural network
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Arrie Kurniawardhani;Rahman, Hanif A.;Paputungan, Irving V. | บทความ/Article |
Rahman, Hanif A.
| Title | Contributor | Type |
|---|---|---|
| Non-invasive automatic drowsiness detection using independently recurrent neural network
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Arrie Kurniawardhani;Rahman, Hanif A.;Paputungan, Irving V. | บทความ/Article |
Paputungan, Irving V.
| Title | Contributor | Type |
|---|---|---|
| Non-invasive automatic drowsiness detection using independently recurrent neural network
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Arrie Kurniawardhani;Rahman, Hanif A.;Paputungan, Irving V. | บทความ/Article |