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DEEP LEARNING TECHNOLOGY FOR FACE RECOGNITION

ABSTRACT
In China, the rapid development of public transportation network construction has been accompanied by a high incidence of traffic accidents caused by sleep-deprived driving. The monitoring of drivers' sleep-deprived driving and the sending out of early warnings has been identified as a field of research with both important theoretical and practical value. This article proposes a fatigue detection algorithm based on facial recognition information fusion. The algorithm extracts facial feature information and head features from the driver's face and fuses them into facial recognition information parameter feature vectors. A set of feature nodes is thus constructed, and an information fusion feature map is constructed according to the interaction information between nodes. The fatigue status is detected using the label propagation strategy, which is more effective than other benchmark detection algorithms. The fatigue detection algorithm has practical value.
KEYWORDS
PAPER SUBMITTED: 2023-11-23
PAPER REVISED: 2024-05-11
PAPER ACCEPTED: 2024-05-12
PUBLISHED ONLINE: 2025-07-06
DOI REFERENCE: https://doi.org/10.2298/TSCI2503007Y
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2025, VOLUME 29, ISSUE Issue 3, PAGES [2007 - 2014]
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2025 Society of Thermal Engineers of Serbia. Published by the VinĨa Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence