This project detects drowsiness using classical computer vision techniques instead of deep learning. It first identifies the face using HOG + SVM (via dlib), then extracts 68 facial landmarks using an Ensemble of Regression Trees (ERT) model. From these landmarks, it calculates the Eye Aspect Ratio (EAR) to measure eye openness. If the EAR drops below a threshold (0.25) for a continuous number of frames, the system concludes that the eyes are closed for too long and triggers a drowsiness alert. App Modes - --> Live Webcam - Click Start to begin real-time detection - Click Stop to release the camera - Runs at ~30 FPS --> Upload Video - Supports `.mp4`, `.avi`, `.mov`, `.mkv` - Click **Run Detection** to process frame by frame - Progress bar shows completion --> Upload Image - Supports `.jpg`, `.jpeg`, `.png`, `.bmp`, `.webp` - Instant single-frame analysis - Shows annotated image with EAR value and status