Deep learning technology for face recognition

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.

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Publication Details

Journal
Thermal Science
Published
2025-01-01
DOI
https://doi.org/10.2298/tsci2503007y
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00

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article

Deep learning technology for face recognition

Bin Yuan
Thermal Science
Sleep and Work-Related Fatigue
article

Deep learning technology for face recognition

Bin Yuan
article en

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.

Thermal ScienceVol. 29(3 Part A)
Qujing Normal University (CN), Yunnan Provincial Department of Education (CN)
Yunnan Provincial Department of Education
Openalex Percentile: Top 86%
Sleep and Work-Related Fatigue
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