# Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
## 2 Related Works  
Judging driver fatigue based on facial state is basically divided into two steps: face detection and state detection. The first step of face state recognition is to detect the face in the image. Before the advent of deep learning technology, Viola et al. [9] introduced what is termed the \"Integral Image\" to represent the feature of the image and combined AdaBoost classifier in a \"cascade\" way to achieve efficient and fast face detection. In recent years, deep learning has met with great success in face detection. Zhang et al. [10] proposed a deep-cascaded multi-task framework (MTCNN) to detect the face. This framework uses cascaded networks to quickly generate and filter candidate facial windows and then generate a final facial bounding box and key face points. Retinaface [11] proposed by Denget al. added a self-supervised mesh decoder branch for predicting pixel-wise 3D shape face information and has surpassed MTCNN in inference efficiency and accuracy. In this study, Retinaface is used as the face detection method. Recent theoretical developments have revealed that the methods of facial state recognition are based on biological vision, traditional machine visual methods, and deep learning. The main research contents of facial state recognition include the eye state (open/close) and the mouth state (open/close). In the biological vision method, Benoit et al. [12] used the bio-inspired vision data to detect face motion. This method calculates the local energy in the video based on the Magno feature of the given video to determine whether the eyes are blinking and the mouth is open. In the traditional machine visual methods, Bakheet et al. [13] improved the HOG feature and used the naive Bayes method to classify the eye state and achieved 85.62% detection accuracy in the NTHU-DDD dataset. Akrout et al. [14] used the optical flow method to detect whether the mouth is open and used Haar wavelets and circular Hough transform to detect the eyes' opening angle. The robustness and accuracy of the deep learning method make it superior to biological vision and traditional machine visual methods. Gu et al. [15] proposed MSP-Net to detect the facial state. MSP-Net can fit multi-resolution input images captured from variant cameras excellently. Ji et al. [16] used MTCNN detect face and design ESR-Net and MSR-Net to detect the facial state. Zhao et al. [17] used the single-shot multi-box detector algorithm to detect the face region and used VGG-16 to classify the facial state. With the development of deep learning, the introduction of the channel attention mechanism in the convolutional neural network has been proved to be effective in improving the accuracy of image classification [18]. Inspired by the above study and the channel attention mechanism, in this study, the RCAN is designed, which includes a series of stacked channel attention blocks to improve the accuracy of facial state recognition. If the driver's head posture changes too much, the detection accuracy of eyes and the mouth will be reduced. Therefore, judging whether the driver's head posture is normal should be included in the scope of fatigue detection. In this study, the estimation of head posture is added to the judgment of the fatigue state. Ruiz et al. [19] trained a multi-loss CNN to predict intrinsic Euler angles. Abate et al. [20] proposed a regression model to estimate the head pose. This method uses a web-shaped model algorithm to encode the head posture and uses a regression algorithm to estimate the Euler angle. In the latest study, Abate et al. [21] combined the fractal image compression characteristics and regression analysis to predict the Euler angle and show its excellent performance in the BIWI dataset and the AFLW2000 dataset. The above methods all use the regression method to solve the head pose problem, which needs more facial landmark input and even extra training.