# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 4 Experiments
### Experimental Results
#### 4.3.4 Comparison and Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Method** & **Accuracy** \\ \hline [PERSON] and [PERSON] [5] & Drowsiness detection based on eye closure and yawning using facial landmarks and Haar cascade classifiers. & 85\% \\ \hline [PERSON] and [PERSON] [6] & Facial landmark detector and Raspberry Pi for a real-time driver alert system. & 83.39\% \\ \hline [PERSON] et al. [7] & Driver drowsiness detection based on eye movement and yawning using facial landmark analysis. & 85\% \\ \hline [PERSON] et al. [10] & Driver drowsiness detection model using convolutional neural network techniques for android application. & 83.33\% \\ \hline [PERSON] et al. [11] & Driver fatigue detection based on convolutional neural networks using EM-CNN. & 93.623\% \\ \hline [PERSON] et al. [12] & Facial landmark predictor and Raspberry Pi3 Model B Module. & 97.1\% \\ \hline [PERSON] et al. [13] & Driver drowsiness detection using deep learning. & 96\% \\ \hline [PERSON] et al. [14] & A deep learning approach to detect driver drowsiness. & 94\% \\ \hline The proposed method with scenario 1 & Facial landmark detection. & 83\% \\ \hline The proposed method with scenario 2 & Deep learning network developed from MobileNet-V2. & 96\% \\ \hline The proposed method with scenario 3 & Deep learning network developed from ResNet50V2. & 97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy comparison of drowsiness detection methods. learning approaches. The former detects the driver's state from blinks and yawns based on the appropriate thresholds defined as dynamically suitable for each driver instead of a predefined threshold for all drivers. The latter uses advanced deep learning techniques for drowsiness detection without estimating the defined thresholds. Two adaptive deep neural networks are proposed and developed on the advanced networks of MobileNet-V2 and ResNet-50V2, which are more efficient in terms of memory and complexity compared to the basic CNN models in the prior works. The use of these proposed deep neural networks is motivated by the fact that they are very good feature extractors, since they can capture and learn relevant features of drowsiness from an image or video at different levels similar to a human brain; (b) instead of focusing on the two regions of the eyes and mouth, the proposed deep neural networks analyze the videos and detect not only two features of blinks and yawns, but also the driver's activities in every frame to automatically learn all features of drowsiness such as head orientation, yawning, eyelid opening, eye blinking, pupil diameter, and gaze direction, etc. for drowsiness prediction. Experiments were conducted on a large dataset to test the effectiveness of the proposed method in accuracy and reliability. Therefore, our method achieves the high accuracy of 97%; (c) leveraging the advantage of transfer learning, the proposed networks are trained on our training dataset. This solves the problem of small training datasets, training time, and keeps the advantage of the deep neural networks; (d) both of our methods can detect drowsiness accurately in the case of the driver wearing a mask (Table 9), while some methods do not consider or provide accuracy in this case. Besides, the proposed methods can recognize drowsiness well for drivers with very small eyes.