# Adaptive Driver Face Feature Fatigue Detection Algorithm Research
## 1 Introduction  
With the continuous development of railroad mileage and the number of cars all over the world, cars have brought speed and convenience to people's lives, while at the same time traffic accidents occur frequently. Fatigued driving is one of the main causes of traffic accidents. According to the China Statistical Yearbook 2021, in 2021, 61,700 people were killed in traffic accidents in China, and about 250,000 people were injured in traffic accidents in China. Among traffic accidents, those caused by fatigued driving account for 20-30% of traffic accidents, especially on highways, where more than 30% of accidents occur due to fatigued driving. Therefore, it is crucial to detect the fatigued driving status and issue the corresponding warning. Fatigue driving refers to the muscle relaxation and mental fatigue that occur after a long period of intense driving, as well as the decrease in hand and foot reaction ability and anticipation ability, which results in slow movements. [PERSON] et al. [1] defined fatigue driving as an excess change from the arousal to the sleep state that occurs when the fatigue state does not change. The identification of fatigued driving status necessitates the use of several disciplines, including medicine, psychology, optics, communication, and computers. As a result, experts and scholars both at home and abroad have concentrated their efforts on detecting fatigued driving. Currently, the most popular form of detection in China is the technique for detecting the fatigue state in a driver's facial features. By observing the driver's eyes movement and head changing positions, the degree of fatigue can be detected [2]. However, because these algorithms frequently fail to take into account the driver's individual traits, they are not reliable and robust. In this paper, we suggest a novel detection technique for individual driver differences in complex situations to address the aforementioned problems. The following are the creative points:  
(1) The enhanced ShuffleNet V2K16 convolutional neural network is used to create a driver facial feature point monitoring architecture. YawDD [3], an open-source dataset, is also used to train the network. The enhanced ShuffleNet V2K16 algorithm improves facial recognition precision, simplifies the network mechanism, decreases computation time, and is simpler to port to mobile devices than other algorithms such as PFLD [4] and PaffPif [5]. (2) The lightweight network structure of ShuffleNet can quickly and accurately gather the driver's facial feature point information to obtain the feature values for assessing the fatigue state by combining ShuffleNet V2K16 with Dlib. (3) The _MAX-MIN_ fatigue detection technique is suggested. The majority of the detection algorithms now in use are based on the _MAR_ and _EAR_ values and employ the driver's eye and mouth feature point thresholds as judgment features to assess whether or not the driver is weary. Based on the analysis of 2000 images from the YawDD dataset, it was observed that the sizes of drivers' eyes and mouths varied. Additionally, the degree of mouth aperture during yawning also exhibited variation. The tiredness level is established by choosing the fatigue threshold that is unique to each driver by comparing the _EAR_ and _MAR_ acquired after 100 frames with the corresponding _EAR-MAX_ and _MAR-MIN_ values. In comparison to earlier detection methods, this method is more accurate and adaptive. This paper is divided into five main sections, as follows:  
The first part mainly introduces the research background and the significance of fatigue driving detection and briefly describes the current research status of fatigue state detection at home and abroad. In view of the shortcomings of the existing research, a new algorithm for fatigue state recognition is proposed in this paper. Finally, the innovation of the proposed algorithm is introduced. The second part describes the existing work related to fatigue detection. Three ideas for fatigue detection solutions are detailed.