## 2 Related Work
The experiments on two different datasets resulted in a mean yaw estimation error of 7.33\({}^{\circ}\). Some visual methods used three-dimensional (3D) tracking. For example, [PERSON] et al. proposed a novel manifold embedding algorithm supervised by both identity and pose information, called synchronized sub-manifold embedding (SSE), for precise 3D pose estimation [40]. The experiments on the 3D pose estimation dataset, CHIL data for CLEAR07 evaluation, showed 6.60\({}^{\circ}\) mean pan estimation error and 8.25\({}^{\circ}\) mean tilt estimation error. [PERSON] et al. presented a new method for static head pose estimation and a new algorithm for visual 3D tracking [41]. The system consisted of three interconnected modules that detected the driver's head, provided initial estimates of the head pose, and continuously tracked its position and orientation with six degrees of freedom. Experimental results showed the mean estimation error of 3.62\({}^{\circ}\) in yaw, and 9.28\({}^{\circ}\) in pitch during the day driving. For night driving, the yaw estimation error of 5.18\({}^{\circ}\) and pitch estimation error of 7.74\({}^{\circ}\) was reported. [PERSON] et al. proposed the head yaw angle estimation, with the advantages of real-time performance, ability to work with low-resolution images, and tolerable to partial occlusions [42]. The proposed model achieved an MAR of 6.65\({}^{\circ}\) in head yaw estimation. [PERSON] et al. proposed a vision-based distraction detection system that used four deep network architectures [43]. Residual Network, VGG-16, AlexNet, and GoogleNet architectures resulted in a performance of frequency in the range of (8-14 Hz) and an accuracy in the range of (86-92%). The GoogleNet outperformed the other network, yielding frequency of 11 Hz at an accuracy of 89%. [PERSON] et al. presented a robust method to determine a head pose by using a multi-loss convolutional neural network to predict the intrinsic yaw angle, pitch angle, and roll angle directly from the image intensity through pose classification and regression [44]. The authors claimed promising results on common pose benchmark datasets. [PERSON] et al. proposed a genetically weighted ensemble of convolutional neural networks on a publicly available dataset with a great variety of distraction postures, reporting an accuracy of 90% [45]. [PERSON] et al. surveyed the face detection techniques and summarized five important models, cascade-CNN-based models, R-CNN-based models, single-shot detector models, feature pyramid network-based models, and transformers-based models [46]. The authors reported an accuracy of 34.5% to 96.5% on the Wider-Face dataset. The authors discussed major challenges in face detection, such as robustness on tiny faces, face occlusion, accurate lightweight models, few-shot face detection, interpretable deep models, and face detection bias reduction. [PERSON] et al. proposed a posture recognition system for drivers' distraction based on a deep recurrent neural network (RNN) that yielded an accuracy of 96.23% and 92.36% on the StateFarm and AUC datasets, respectively, [47]. [PERSON] et al. proposed head pose estimation from a single image [48]. The authors proposed a fine-grained structure mapping for spatially grouping features before aggregation. The fine-grained structure provides part-based information and pooled values. The authors claimed to have results comparable to the most recent methods. [PERSON] et al. explored the machine learning algorithms to detect driver distraction due to smartphone usage [49]. The authors reported that more than 95% accuracy can be obtained using CNN and gradient boosting methods. [PERSON] et al. proposed a driver fatigue detection system based on the residual channel attention network and head pose estimation [50]. The authors reported 98.62% accuracy of detecting eye state and 98.56% of detecting mouth state. The authors also proposed the perspective-n-point method to estimate excessive deflection of head. [PERSON] et al.