# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 3 Intelligent Detection Methods for Driver Fatigue and Distraction
### Intelligent Detection Methods Based on Driver's Facial Features
[38] used the ShuffleNet V2K16 neural network for driver facial recognition, reducing the impact of the detection environment, and by comparing the current frame's EAR and MAR values with the maximum EAR value and minimum MAR value of the previous 100 frames, they minimized the impact of individual differences, greatly improving experimental accuracy. Figure 4: Extracting keyframes for subtle facial changes. (© (2020) IEEE. Reprinted, with permission, from [35]). Figure 3: General flowchart of intelligent detection method based on facial features. Some researchers have utilized cutting-edge deep learning networks, significantly enhancing detection precision. [PERSON] et al. [39] introduced a dual-stream neural network into the Multi-Task cascaded Convolutional Neural Network (MTCNN), combining the static and dynamic features of drivers to improve detection performance, achieving an accuracy rate of 97.06%. Moreover, by using Gamma correction, they enhanced the accuracy of nighttime detection, effectively overcoming the challenges of detection in night conditions. [PERSON] et al. [40] incorporated two InceptionV3 modules into the MTCNN network to extract the features of eye and mouth sub-samples, significantly improving the detection precision of local features. Although this method performs well in local feature recognition, its efficiency in processing large-scale data remains a consideration. In addressing delays caused by complex algorithms, [PERSON] et al. [41] developed a lightweight driver state monitoring system that realized end-to-end detection, significantly improving detection efficiency. This method simplified the detection process, but may have sacrificed some recognition precision. [PERSON] et al. [42] used near-infrared cameras instead of traditional RGB and infrared cameras and established an integrated deep learning network model, which ensured the algorithm's accuracy while reducing the hardware computational requirements, offering a way to achieve efficient detection in resource-limited situations. [PERSON] et al. [43] addressed the issue that most detection methods cannot detect distracted behaviors not included in the training set well by using a semi-supervised approach with a dual-stream backbone network design, ensuring the model's lightweight structure while enhancing its generalization capability. Table 1 presents a comparative analysis of recent studies on new detection methods based on facial features. Overall, such methods, under experimental conditions, boast a high accuracy and convenience, becoming the primary choice for developing driver state monitoring systems [44]. However, the actual application of these methods still faces several challenges, mainly as follows:  
Figure 5: The architecture of detection methods considering individual differences among drivers [37]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
\begin{tabular}{c} Adaptive detection of distracting \\ behaviors not included in the training \\ set to ensure lightweight and enhance \\ generalization capability. 1. The actual precision of facial feature extraction is easily affected by the environment. Facial appearance may vary significantly under different lighting conditions, angle changes, and facial expressions, making it difficult to maintain stability and accuracy in facial feature extraction. Additionally, face occlusions, wearing glasses, and makeup can also affect the effectiveness of facial feature extraction. 2. In detection methods based on facial features, most use facial feature parameters such as EAR and MAR, etc., to judge fatigue or distraction states. However, the setting of thresholds often relies on subjective experience, lacking objectivity and unified standards. 3. Detection methods based on facial features are greatly affected by individual driver differences.