# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 3 Methods  
This paper presents an efficient detection model that uses Dlib's pre-trained face detector to identify faces and extract features using Dlib's 68-point facial landmarks. The model estimates the eye closure ratio (ECR) and mouth aperture ratio (MAR) metrics to assess indicators of drowsiness, such as sleepy eyes and yawning. The assessment of these metrics determines the drowsiness status of a driver. This section describes the proposed systems and their implementation, encompassing the design phases, the flow chart of the detection model, and the algorithms guiding its operation. Modern drowsiness detection systems increasingly rely on various sensors to monitor drivers' physiological and behavioral cues. In addition to video-based methods like the eye closure ratio (ECR) and mouth aperture ratio (MAR), sensor-based approaches offer valuable complementary data: there are physiological sensors such as electroencephalogram (EEG) sensors which capture brain wave activities, which have been shown to correlate strongly with drowsiness levels. In some systems, electrodes positioned on the scalp or embedded in headtests measure these signals and provide real-time feedback for drowsiness detection. Additionally, heart rate variability sensors monitor shifts in autonomic nervous system activity, often preceding visible signs of drowsiness. These physiological metrics can significantly enhance the accuracy of drowsiness detection systems. Also, infrared sensors combined with eye-tracking technologies have been increasingly used to monitor eye movement patterns and blink rates more accurately than visual methods. These sensors provide continuous and highly accurate measurements, even in low-light conditions, ensuring reliable detection regardless of external conditions. The data collected from these sensors can be integrated with existing algorithms to improve sensitivity and reduce false positives in drowsiness predictions. Recent advancements have introduced wearable technologies, such as smartwatches and fitness trackers, that monitor physiological metrics like heart rate, skin temperature, and galvanic skin response. These devices can alert drivers about impending drowsiness by analyzing trends in these metrics over time, offering a proactive approach to driver safety. Furthermore, combining data from various sensors--physiological, environmental, and visual--through multi-modal sensor fusion techniques can lead to more accurate and reliable drowsiness detection. By leveraging the strengths of different sensors, the system can enhance detection accuracy and resilience against false alarms, particularly in complex driving conditions. The computational complexity of the proposed methods is assessed based on time and space considerations, showing minimal computational overhead attributed to several factors: leveraging pre-trained Dlib for facial landmark detection, resizing images, and computing only two numerical parameters. The reduction in image dimensions, achieved through techniques such as resizing, cropping, and downsampling, significantly decreases the data volume requiring processing, thus contributing to faster processing times and reduced memory usage, which is crucial for real-time applications demanding quick response times. Implementation costs are kept low through the use of a durable, cost-effective camera, efficient software leveraging pre-trained models like Dlib, and optimization techniques that streamline processes, eliminating the need for high-end hardware. The methods are easily scalable to run on multiple standard CPUs, ensuring broad accessibility without substantial investment in specialized hardware. Moreover, deploying the system on a high-performance Central Processing Unit (CPU) provides notable benefits such as broad availability, cost-effectiveness, real-time processing capabilities, and energy efficiency, given that CPUs generally have lower power consumption compared to GPUs.