# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 1 Introduction  
Every day, lives are lost or irrevocably changed due to traffic accidents, with a fatal collision occurring approximately every 25 s [1]. These incidents can be attributed to a variety of factors including alcohol impairment, driver drowsiness, fatigue, distractions, and mechanical malfunctions. According to data from the U.S. National Highway Traffic Safety Administration (NHTSA), nearly 100,000 accidents and 1500 fatalities annually can be directly linked to drowsy driving [2]. The impact of these accidents extends beyond the statistics, affecting families, communities, and society at large. Therefore, addressing the issue of driver drowsiness is crucial for enhancing road safety and preventing needless tragedies. This study places a primary emphasis on drowsiness as it encompasses various other contributing factors to road accidents. For instance, drowsiness can affect any driver, including those under the influence of alcohol, and may lead to lapses in concentration. Indicators of drowsiness include reduced focus, increased eye blinking, frequent yawning,and even more subtle signs like minor infractions of traffic laws and difficulty maintaining a steady head position. Drowsiness detection technology is crucial for enhancing automobile safety by alerting drivers and potentially preventing accidents. Different methods for detecting drowsiness exist, categorized into vehicular, physiological, and behavioral parameters. Behavioral methods monitor actions such as the eye closure ratio (ECR), blinking frequency, and facial expressions, while vehicular techniques analyze the vehicle's movements, and physiological methods measure physical indicators like heart rate. Among these, behavioral methods are preferred for their cost-effectiveness and simplicity, despite challenges related to data processing and feature extraction efficiency. Several studies have explored integrating physiological parameters like heart rate variability (HRV), pupil dilation, and skin conductance alongside behavioral indicators for improved drowsiness detection. These approaches offer promising results but often require specialized sensors or wearable devices, which may increase system complexity. This study prioritizes cost-efficiency through non-invasive facial metrics while recognizing the potential for multi-modal integration in future work. This research focuses on optimizing the accuracy of behavioral detection methods while considering the trade-offs between cost, time, and accuracy. Additionally, a novel approach known as the adaptive frame threshold is introduced to improve drowsiness detection during extended journeys, a scenario often overlooked in the existing literature. This innovative method aims to address specific challenges encountered during long-duration driving, offering potential improvements in accuracy and reliability. Having established driver drowsiness as a critical factor contributing to road accidents globally, this study aims to develop advanced detection systems to enhance vehicular safety. Recent research has seen the emergence of innovative approaches leveraging deep learning frameworks, machine learning algorithms, and computer vision techniques to detect and mitigate the risks associated with drowsy driving. The scheme of this research outlines the workflow for the study on drowsiness detection, shown in Figure 1, with an emphasis on the innovative aspects and improvements over the state of the art:  
1. Problem identification: highlighting the impact of drowsy driving and the need for effective detection methods;
2. Dataset collection: using video data with annotated drowsy and non-drowsy states for behavioral analysis. Selection and preprocessing of video datasets for behavioral analysis;
3. Feature extraction: utilizing tools like Dlib to measure the eye closure ratio (ECR) and mouth aperture ratio (MAR) as behavioral indicators;
4. Static threshold method: introducing a fixed threshold approach using ECR and MAR to detect drowsiness in real time.