# Adaptive Driver Face Feature Fatigue Detection Algorithm Research
## 2 Related Works  
Fatigue driving recognition methods based on deep learning have received a lot of attention from many industries, but the majority of these appeared one after another only after 2015 and were basically divided into three model design ideas. The first research line is to identify different drivers by using image classification models. [PERSON] et al. [6] proposed a multi-stream CNN model, designed three shallow volumes and networks with different sensory fields, and realized multi-scale exchange by a feature map fusion strategy. The model was tested on the publicly available State Farm dataset with a precision of 86.4%. [PERSON] et al. [7] obtained information from multiple channels, such as grayscale, gradient, and optical flow from the input frames, and extracted the temporal and spatial information contained in the feature maps by 3D convolution. The feature maps were fed to the attention mechanism module, and the feature weights were optimized. The SVM (Support Vector Machine) classifier output was used to determine the driving state. The paper also carried out some research on protecting the driver's facial privacy and security. The recognition rate could reach 95% on the self-built dataset (SBD). Image classification models usually take specific frames as input and are trained end-to-end to determine different driver behavior classes directly. The advantages of this type of model are its simple and clear network structure and fast computation. The drawbacks of this type of model are that it does not utilize dynamic information between frames and has slightly lower precision, which is suitable for embedded platforms. [PERSON] et al. [8] used a motion capture system to detect the driver's head posture motion to measure whether the driver was fatigued or not. The second line of research is to use target detection models to localize specific targets and achieve person-to-person discrimination. [PERSON] et al. [9] proposed a fatigue driving detection algorithm based on deep learning and driver facial multi-metric fusion, using an improved multi-task cascaded convolutional neural network (MTCNN) to quickly and accurately locate the face and detect the key points of the face, fusing the study closure rate, mouth expansion rate, and head non-positive face rate features to determine whether the driver is fatigued or not. The proposed algorithm based on the homemade dataset of [PERSON] et al. [10] collected actual eye movement parameters from 36 drivers using an \"eye-tracking device\" and used a driving simulator to simulate the actual eye movement parameters. PERCLOS, combined with the SSS table, was used to determine the fatigue level threshold for different monotonic driving scenarios. Finally, a deep learning method based on LSTM (long short-term memory) was used to build recognition models for drivers with different fatigue levels to detect their actual levels. The recognition rate of the established fatigue recognition model can reach 97.8% for the total recognition rate of drivers, which is higher than the recognition precision of traditional machine learning methods. The third research idea is to identify different driver behaviors using video classification models. [PERSON] et al. [11] used the I3D model to extract spatiotemporal representations from video sequences to discriminate driver behaviors. [PERSON] et al. [12] proposed a TCDCN deep-learning-based multifaceted feature fusion fatigue detection algorithm that introduces a new face-tracking algorithm to improve precision. Using EAR, MAR, and PERCLOS and setting the weights, the threshold to determine the fatigue status can be found. It can identify driver drowsiness and yawning in real time. In contrast to image classification models and target detection models, the input to video classification models is a video sequence. These models effectively use inter-frame motion information and can extract spatiotemporal representations of driver behavior with higher precision than the first two types of models.