# Adaptive Driver Face Feature Fatigue Detection Algorithm Research
## 4 Experiment and Analysis
### Actual Scene Detection Experiment  
Six healthy car drivers were also chosen to record roughly 8 min of driving videos in a genuine driving environment for detection in order to confirm the efficacy of the _MAX-MIN_ algorithm developed in this work for driver fatigue state detection in realistic circumstances. The drivers can be seen in the films conversing, yawning, and performing other real-life activities. For the test, a simulated tiredness video lasting 30 s was chosen at random. In a real-world scenario, Figure 12 shows the fatigue detection technique employing a single _EAR_ and _MAR_. Figure 13 shows the _MAX-MIN_ proposed in this paper's approach for detecting driver weariness under comparable circumstances.  
In this paper, testing was carried out using the open dataset YawDD and the custom-built dataset SBF. Male and female test results were adopted, respectively. Table 3 displays the comparative outcomes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gender** & **Category** & **Number** & **The Precision of MAX-MIN Algorithm** \\ \hline \multirow{4}{*}{Female} & Eye Open & 2032 & 98.7\% \\  & Eye Closed & 1258 & 98.5\% \\  & Mouth Open & 5862 & 99.0\% \\  & Mouth Closed & 2684 & 98.6\% \\  & Eye Open & 1768 & 98.6\% \\ \multirow{2}{*}{Male} & Eye Closed & 1006 & 98.7\% \\  & Mouth Open & 6184 & 99.1\% \\ \multirow{2}{*}{Female} & Mouth Closed & 3531 & 98.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision of detection of different states based on _MAX-MIN_ algorithm.  
Figure 12: The effect of the original detection method on the actual scene. (**a**) Female normal driving; (**b**) Female driver yawning; (**c**) Female drivers close their eyes; (**d**) Male normal driving; (**e**) Male driver yawning; (**f**) Male drivers close their eyes.  
The above table shows that the algorithm in this paper can accurately and quickly determine the driver's fatigue status. Table 4 shows the results of a comparison of the _MAX-MIN_ algorithm proposed in this paper with other algorithms.  
The experiments show that the proposed _MAX-MIN_ algorithm outperforms other algorithms in the literature. The precision of driver fatigue detection using the proposed algorithm in this paper is 98.8%, the recall rate is 90.2%, and the F-score is 94.3%. The results show that the algorithm proposed in this paper can accurately determine the driver's fatigue state.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & _Precision_ & _Recall_ & _F-Score_ \\ \hline
3D head pose estimation [15] & 98.19\% & 97.3\% & 97.74\% \\ SVM + Adaboost [16] & 85.28\% & NA & NA \\ TCDCN + KNN [12] & 95.1\% & NA & NA \\ MTCNN + LSTM [17] & 93\% & NA & NA \\
3D-CNN + Attention [7] & 95\% & 95\% & 95\% \\ Proposed & 98.8\% & 90.2\% & 94.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of our results with those in the literature.  
Figure 13: The effect of the actual scene _MAX-MIN_ detection method. (**a**) Female normal driving; (**b**) Female driver yawning; (**c**) Female drivers close their eyes; (**d**) Male normal driving; (**e**) Male driver yawning; (**f**) Male drivers close their eyes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Gender** & _Precision_ & _Recall_ & _F-Score_ \\ \hline \multirow{2}{*}{YawDD} & Female & 99.1\% & 89.7\% & 94.2\% \\  & Male & 98.7\% & 90.2\% & 94.3\% \\  & Female & 98.8\% & 90.3\% & 94.4\% \\ \multirow{2}{*}{SBD} & Male & 98.6\% & 90.6\% & 94.4\% \\ Average & & 98.8\% & 90.2\% & 94.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics of the _MAX-MIN_ algorithm for detection in YawDD and SBD datasets.