# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 5 Conclusions  
Most of the traditional methods for drowsiness detection are based on behavioral factors, while some require expensive sensors and devices to measure sleepiness, and may even interfere with the driving process, distracting drivers. Therefore, in this paper, we propose two methods with three scenarios for driver's drowsiness detection systems. The proposed method with scenario 1 uses facial landmarks to detect drowsiness. This method analyzes the videos and detects drivers' faces in every frame using image processing techniques. Facial landmarks are determined in order to compute the eye aspect ratio (EAR) and the mouth-opening value (\(LIP_{distance}\)) to detect drowsiness based on adaptive thresholds. We propose an improvement of the eye-opening threshold for each driver without using a pre-defined threshold for everyone, as in preceding studies. In each video frame, the frequent detection of eye blinking and yawning will help to properly compute the drowsiness level. The driver is alerted when the blink and yawn thresholds reach the adaptive maximum thresholds. However, the drowsiness detection based on blinking and yawning is not accurate enough, since drowsiness has different surrounding factors. Therefore, we propose method 2, with two scenarios for a drowsy alert system using deep learning techniques with the transfer learning approach. We design two adaptive deep neural networks developed from MobileNet-V2 and ResNet-50V2 for scenarios 2 and 3, respectively. Method 2 analyzes the videos and detects the driver's activities in every frame to automatically learn all features for drowsiness detection. It takes advantage of deep neural networks to extract all features and movements of the head and face. This method does not require the definition of input thresholds as in method 1, especially the eye-opening and yawning thresholds. Additionally, method 2 uses a combination of many typical signs of drowsiness to give accurate results, such as eye-opening, head movements, eyebrows, mouth, etc. Moreover, we leverage the advantage of transfer learning to pre-train the proposed networks on datasets of Bing Search API, Kaggle, and RMFD. We then use the pre-trained weights and re-train them on our training dataset to fine-tune the parameters of these networks. This helps to solve the problem of small training datasets and gives a fast training time. Experiments were conducted to test the efficacy of the proposed approaches. The results show that the proposed methods can achieve a high accuracy of 97% using deep learning techniques. Method 2 with scenario 3 provides more accurate results than scenario 2 and method 1 because it detects most of the drowsiness in various experimental contexts. Some experimental comparisons between the proposed methods and the preceding methods are made to discuss the advantage and limitation of the proposed methods. We also highlight the limitations of the preceding studies that affect the effectiveness in actual applications. Method 2 improves the accuracy of drowsiness detection compared with eyelid and mouth movement-based methods. From the above results, the proposed method using deep learning techniques can be useful for monitoring the fatigue of drivers to give early warning of falling asleep, avoiding unfortunate traffic accidents. The experimental results show that the method is feasible and adaptable to the development of applications for doze alert systems, especially mobile applications. This study helps to prevent automobile accidents caused by falling asleep behind the wheel. However, a key requirement for drowsiness detection is that the solutions work in real time or near real time. Thus, we will make an improvement in our further research using big data analysis to build a real-time system for drowsiness detection. Conceptualization, A.-C.P. and T.-C.P. ; methodology, A.-C.P., N.-H.-Q.N. and T.-C.P. ; software, A.-C.P. and N.-H.-Q.N. ; validation, T.-N.T. and T.-C.P. ; formal analysis, A.-C.P. and N.-H.-Q.N. ; investigation, A.-C.P. and T.-C.P.