# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 4 Experiments
### Experimental Results
#### 4.3.4 Comparison and Discussion  
Table 9 represents a comparison of the accuracy of the drowsiness detection methods. The accuracy of these methods achieves from 83% to 97.1%, while the proposed method reaches the accuracy of 97%. Most of the preceding methods in [5; 6; 7; 10; 11; 12; 13; 14] focus on analyzing the eye and mouth areas to detect blinks and yawns. They use Haar cascade classifiers for facial landmark detection and deep learning or machine learning for drowsiness prediction. These are classical approaches that use hand-engineered features to estimate the state of the eyes and mouth, which have revealed many limitations on drowsiness detection. Besides, Haar cascade classifiers tend to be prone to false positive detections and require parameter tuning when they are applied for detection. Although these methods reach the accuracy of 96-97.1% for the experiment, they have many limitations on practical systems because they only rely on two key features of blinks and yawns for drowsiness detection without considering other physiological factors. Moreover, the blink and yawn time thresholds to detect drowsiness are pre-defined for all drivers, while these thresholds are different for each driver. However, drowsiness is a natural phenomenon in the human body that happens due to different factors causing distraction. Therefore, drowsiness prediction is not accurate enough if it is only based on blinks and yawns. Hence, we need take some signs of head and eye movements (and other physiological features) into account to be able to make more accurate predictions. We propose an efficient method that uses deep learning with two advanced deep neural networks developed from MobileNet-V2 and ResNet-50V2. \begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Scenario** & **1** & **2** & **3** \\ \hline a. & & & \\ b. & & & \\ c. & & & \\ d. & & & \\ \hline \end{tabular}
\end{table} 
Table 8: Some experimental results of drowsiness detection for three scenarios. In our work, we address a drowsy driver alert system that has been developed by the techniques of the facial landmarks with adaptive thresholds and deep learning with the transfer learning approach. Through experimental results and Table 9, the training results in three proposed scenarios, 1, 2 and 3, have the accuracy of 83%, 96%, and 97%, respectively. Figure 14 shows that the accuracy in scenario 3 is more stable and the loss is also lower than scenarios 1 and 2 at 30 epochs. It can be seen that scenario 3 gives better results in terms of accuracy and loss but its training time is longer than scenario 2. We can conclude that scenarios 2 and 3 give better results than scenario 1. The outstanding advantage of the proposed method with scenario 1 is that is proceeds with calculations and gives quick results. The limitation of the method is that the determination of the eye-opening threshold is not accurate when the driver does not look at the camera upon the system starting. In addition, the prediction only based on eye movements is not accurate, since it leads to confusion if the driver blinks continuously or has too-small eye-openings. Moreover, the determination of input thresholds that affects the prediction accuracy even causes the failure of no detection for doze detection. In a case where the driver only closes his eyes but is not sleepy, it is difficult for scenario 1 to give accurate results. The drowsiness detection based on blinks and yawns is not accurate enough. Hence, we need take some signs of movements of the head and eyes (head titled, inability to keep the head straight) into account to be able to make more accurate predictions, and this has been implemented in scenarios 2 and 3. The experimental results have highlighted the main advantage of the proposed method as follows: (a) two proposed methods with three scenarios use facial landmark and deep