Real-Time Driver Drowsiness And Fatigue Detection Using Facial Landmarks And Deep Learning: A Review And Webcam-Based System Design

Driver fatigue is one of the most common and preventable causes of road accidents. Long hours, monotonous highways and lack of rest reduce alertness, often without the driver noticing. Many existing solutions depend on wearable physiological sensors or expensive vehicle hardware that most drivers do not have. This paper reviews driver drowsiness detection approaches and presents the design of a real-time system that needs only an ordinary webcam. Behavioural, physiological and vehicle-based methods are compared, and vision-based techniques are examined in detail, including face detection, dense facial landmark extraction with MediaPipe Face Mesh, eye aspect ratio and mouth aspect ratio measures, PERCLOS and convolutional neural networks that verify eye closure and yawning. Public datasets for drowsiness and yawning are discussed. In the proposed design, OpenCV captures video, Face Mesh locates 468 landmarks, ratios are computed in every frame, and a CNN confirms closed eyes and yawns to reduce false alarms; if signs persist across consecutive frames, an audible alarm is raised. Research gaps, objectives, a comparison of methods and challenges such as night-time lighting, spectacles, head pose and individual variation are discussed.