## 1 Introduction  
Traffic safety remains a paramount concern in our society [1]. Factors such as drunk driving [2], speeding, and fatigue driving [3] not only endanger the lives of drivers but also pose a threat to public transportation safety. Unlike drunk driving and speeding, which are regulated by laws and policies, issues related to fatigue driving can be mitigated through instantaneous detection and warning technologies [4]. Fatigue driving impairs driving stability and decision-making ability [5], representing a significant factor contributing to traffic accidents. According to previous studies, fatigue driving is linked to approximately 16.5% of fatal crashes and 12.5% of injury-related collisions in the United States. Globally, it may account for up to 20% of all traffic accidents [6]. This article aims to mitigate traffic accidents attributable to fatigue driving by proposing a fatigue driving detection algorithm that takes into account individual driver characteristics. Currently, the detection of fatigue driving is mainly divided into subjective and objective methods [7]. The subjective method relies on questionnaires such as the Stanford 'Sleepiness Scale (SSS) [8], Visual Analog Scale (VAS) [9], and Karolinska Sleepiness Scale (KSS) [10], which depend on drivers' subjective perceptions. Due to its subjective nature, this method cannot serve as a standardized approach for detecting fatigue driving. Objective methods involve utilizing auxiliary tools to detect the driver's physiological characteristics [11; 12], vehicle information [13; 14], or facial features [15; 16] to ascertain fatigue driving. Physiological feature detection using wearable sensors is not only employed in medical contexts [17], but also extensively utilized in fatigue driving detection. While wearable sensors are undeniably becoming smaller, lighter, and more accurate [18; 19; 20], their costliness and potential discomfort during prolonged use may hinder the widespread adoption of fatigue detection based on physiological characteristics [21; 22]. For instance, electroencephalography (EEG) headsets are commonly priced between USD 1000 and 25,000, presenting a substantial economic barrier for widespread deployment. Moreover, user experience studies have reported that extended use of ECG chest straps can cause skin irritation and physical discomfort, which may reduce driver compliance and affect the overall effectiveness of fatigue detection systems [23]. Techniques that indirectly evaluate driver fatigue levels by monitoring alterations in vehicle speed and steering entail diverse data collection and analysis procedures, thereby markedly escalating the complexity of the detection system. Fatigue detection based on facial features typically involves non-contact methods that minimize interference with the driver and directly reflect the driver's fatigue state, becoming the mainstream direction of related research [24]. This method usually considers feature variations from the eyes, mouth, and head pose. [PERSON] [15] proposed a method utilizing deep learning techniques to analyze facial features, achieving high accuracy in detecting driver fatigue. However, challenges arise when facial features are obscured, such as when drivers wear masks or cover their mouths while yawning or coughing. [PERSON], [PERSON] et al. [25] developed a multi-attention fusion model that improves fatigue detection performance by enhancing feature extraction across multiple facial regions. However, this approach still faces limitations in detecting fatigue when significant facial occlusions occur, such as in poor lighting conditions or when the driver's head is tilted at extreme angles. Detection based on human eye features provides the most intuitive characterization of fatigue. [PERSON], [PERSON] et al. [1] reported a method for detecting fatigued driving by eye tracking and dynamic template matching using the Hue-Saturation-Intensity (HSI) color model and the Sobel edge operator with an accuracy of up to 88.9%.