Fatigue Detection Method Based on Facial Features and Head Posture

Authors

Rongxiu Lu, 1.School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China;2.Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang 330013, China;Follow
Bihao Zhang, 1.School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China;2.Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang 330013, China;
Zhenlong Mo, 3.School of Transportation and Logistics, East China Jiaotong University, Nanchang 330013, China;

Abstract

Abstract: Aiming at the of the single fatigue characteristics, low robustness and inability to customize fatigue thresholds for different drivers of fatigue detection methods, a method based on facial features and head posture is proposed. In face detection and face key point positioning HOG feature operator and regression tree algorithm are used. In head posture estimation, head posture Euler angle is estimated by combining the face key points with the coordinate system transformation. In fatigue feature extraction, a deep residual neural network model is established to extract the eye fatigue features, which the eye, mouth aspect ratio and head posture Euler angle. The fatigue characteristics of eyes, mouth and head are used to establish the support vector machine models for different drivers to provide the early fatigue driving warning. Experiments show that on YawDD and self-built fatigue simulation data sets, the method shows high accuracy and robustness, and can provide better fatigue warning when a certain fatigue feature detection is blocked.

Recommended Citation

Lu, Rongxiu; Zhang, Bihao; and Mo, Zhenlong (2022) "Fatigue Detection Method Based on Facial Features and Head Posture," Journal of System Simulation: Vol. 34: Iss. 10, Article 18.
DOI: 10.16182/j.issn1004731x.joss.21-0583
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss10/18

First Page

2279

Revised Date

2021-08-09

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0583

Last Page

2292

CLC

TP391

Recommended Citation

Rongxiu Lu, Bihao Zhang, Zhenlong Mo. Fatigue Detection Method Based on Facial Features and Head Posture[J]. Journal of System Simulation, 2022, 34(10): 2279-2292.

DOI

10.16182/j.issn1004731x.joss.21-0583