THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

Bin Yuan

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
fatigue test, face recognition, information fusion, deep learning
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]
REFERENCES
  1. Shahrndin, N. S. N., Sidek, K. A., A Review of ECG Data Acquisition for Driver Drowsiness Detection, Compusoft, 9 (2020), 7, pp. 3749-3754
  2. Girvan, M., Newman, M. E. J., Community Structure in Social and Biological Networks, Proceedings of the National Academy of Sciences, 99 (2002), 12, pp. 7821-7826
  3. Mott, G. E., et al., Efficient Driver Drowsiness Detection at Moderate Levels of Drowsiness, Accident Analysis and Prevention, 50 (2013), 1, pp. 341-350
  4. Li, Z. J., et al., Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions, Sensors, 17 (2017), 3, pp. 495-507
  5. Wang, W., et al., A Survey of Zero-Shot Learning: Settings, Methods, and Applications, ACM Transactions on Intelligent Systems and Technology, 10 (2019), 2, pp. 1-37
  6. Furugori, S., et al., Estimation of Driver Fatigue by Pressure Distribution on Seat in Long Term Driving, Review of Automotive Engineering, 26 (2005), 1, pp. 53-58
  7. Wei, Y., et al., Multi-Vehicle Detection Algorithm through Combining Harr and Hog Features, Mathematics and Computers in Simulation, 79 (2019), 8, pp. 130-145
  8. Bila, C., et al., Vehicles of the Future: a Survey of Research on Safety Issues, IEEE Transactions on Intelligent Transportation Systems, 18 (2017), 5, pp. 1046-1065
  9. Mandal, B., et al., Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State, IEEE Transactions on Intelligent Transportation Systems, 18 (2017), 3, pp. 545-557
  10. Nie, B. S., et al., Experimental Study on Visual Detection for Fatigue of Fixed-Position Staff, Applied Ergonomics, 65 (2017), Nov., pp. 1-11
  11. Yao, S., et al., Eye State Detection Method Based on LBP, Application Research of Computers, 32 (2015), 6, pp. 1897-1900
  12. Li, M., et al., Structure-Revealing Low-Light Image Enhancement via Robust Retinex Model, IEEE Transactions on Image Processing, 27 (2018), 6, pp. 2828-2841
  13. LeCun Y, et al., Deep Learning, Nature, 521 (2015), 7553, pp. 436-444
  14. Fu, X., et al., A Probabilistic Method for Image Enhancement with Simultaneous Illumination and Reflectance Estimation, IEEE Transactions on Image Processing, 24 (2015), 12, pp. 4965-4977
  15. Di, W., et al., Driver Eye Feature Extraction Based on Infrared illuminator, Journal of Transport Information & Safety, 27 (2009), 1, pp. 79-82
  16. Chen, X., et al., Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, 11 (2014), 10, pp. 1797-1801
  17. Gu, J., et al., Recent Advances in Convolutional Neural Networks, Pattern Recognition, 77 (2018), 12, pp. 1620-1634
  18. Zhang, K., et al., Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks, IEEE Signal Processing Letters, 23 (2016), 10, pp. 1499-1503
  19. Song, F., et al., Eyes Closeness Detection from Still Images with Multi-Scale Histograms of Principal Oriented Gradients, Pattern Recognition, 47 (2014), 9, pp. 2825-2838
  20. Zhu, X., Goldberg. A. B., Introduction to Semi-Supervised Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, 3 (2009), 1, pp. 1-130
  21. Graves, A., et al., A Novel Connectionist System for Unconstrained Handwriting Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (2008), 5, pp. 855-868
  22. Liu, W., et al., Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection, Future Internet, 11 (2019), 5, 115
  23. Kuang, M. H., et al., A Hybrid Deep Learning Approach for Sentiment Analysis in Product Review, Facta Univ.-Ser. Mech., 21 (2023), 3, pp. 479-500
PDF VERSION [DOWNLOAD]
Deep learning technology for face recognition

2025 Society of Thermal Engineers of Serbia. Published by the Vinča 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