# Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
## 4 Conclusions  
Research on fatigue driving detection technology is the top priority for research in reducing traffic accidents caused by fatigue, which is of great significance to traffic safety. As far as we know, the current driving fatigue detection generally makes a single detection based on the state of the eyes or mouth, and there is little research on the comprehensive detection based on facial multi-feature fusion. However, the accuracy of single feature fatigue detection is inadequate, and it is prone to false recognition. In order to solve the above problems, this paper mainly carries out the following research:  
1. A comprehensive driving fatigue detection method based on multi-facet feature fusion based on TCDCN deep learning is proposed, which makes comprehensive use of facial features (such as eyes and mouth) to fuse visual information and effectively improve the accuracy of driving fatigue detection. The experimental results show that the proposed algorithm can greatly improve the detection accuracy of driving fatigue under various driving conditions.
2. The EAR/MAR/PERCLOS calculated through TCDCN is a more concise solution regardless of whether the driver wears glasses or not. No matter whether the driver wears glasses or not, the TCDCN method can accurately identify the eye feature points. This feature is very important for subsequent driving fatigue detection.
3. The fatigue detection algorithm proposed in this paper can not only effectively identify the driver's fatigue state such as dozing and yawning in real time, but also automatically determine the level of fatigue for a driver: mild, moderate, or severe fatigue.
4. In the future research work, we will focus on the following aspects: (1) the above research will be installed on a real vehicle to further verify the recognition effect of similar driving conditions at night; and (2) we will add other visual features to fuse information, such as the driver's head posture.  
Conceptualization, T.Z. and C.Z.; methodology, T.W.; software, J.L.; validation, Z.O. and W.L.; analysis, T.Z. and X.N.; investigation, H.L. and C.Z.; resources, T.Z.; data curation, C.Z.; writing--original draft preparation, T.Z.; writing--review and editing, T.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l} \hline
**Item** & **Description** & **Scale** \\ \hline
1 & Do you feel drowsy? & 1–4 \\
2 & Do you have difficulty with thinking clearly? & 1–4 \\
3 & Do you have difficulty with making an immediate response to a question? & 1–4 \\
4 & Do you have difficulty with control over your muscles? & 1–4 \\
5 & Do you have difficulty with focusing? & 1–4 \\ \hline \end{tabular} Note: 1 indicates not at all; 2 a little; 3 moderate; and 4 severe.  
\end{table}
Table 1: The self-report assessment of fatigue.  
**Funding:** This work was supported in part by the Teaching Quality and Reform of Higher Vocational Education Project of Guangdong Province (GDJG2019463), the Characteristic innovation project of Guangdong Provincial Department of Education (2019 KTSCX201), the Hebei Province Key R & D Project (17394501D), the Zhaoqing Research and Development Technology and Application of Energy Conservation and Environmental Protection Ecological Governance (2020 SN004), and the Quality Engineering Project of Zhaoqing University (zlgc202034), Special Projects in Key Fields of Colleges and Universities of Guangdong Province (New Generation Information Technology: 2021 ZDZX1061).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Acknowledgments:** All individuals included in this section have consented to the acknowledgement.  
**Conflicts of Interest:** The authors declare no conflict of interest.