MATLAB Code Implementation for Fatigue Detection

Fatigue detection holds significant application value in fields such as driving monitoring and industrial safety. A MATLAB-implemented fatigue detection system typically integrates multiple computer vision technologies, with its core logic following these key steps: Face localization based on skin color The system first performs rough face localization using skin color models in HSV or YCbCr color space. This approach effectively filters background interference and quickly locks onto facial regions. In MATLAB implementation, color threshold segmentation combined with morphological processing can optimize detection results, followed by candidate face screening using regional characteristics (such as area and aspect ratio). Precise eye region localization Within the detected facial ROI (Region of Interest), eye positions are located using Haar cascade classifiers or grayscale projection methods. Due to distinct eye features (such as low grayscale values and dense horizontal edges), vertical integral projection can determine the vertical boundaries of eyes. Pupil detection using Hough transform circle detection After performing edge detection (using operators like Canny) on the eye region, Hough transform detects circular contours to locate pupils. MATLAB's `imfindcircles` function directly implements this functionality, requiring appropriate radius range parameters to adapt to different eye sizes at varying distances. Blink frequency analysis and fatigue determination Blink frequency is calculated through pupil state changes across consecutive frames: - A blink is recorded when the pupil occlusion area by the upper eyelid exceeds a threshold - Count blink occurrences per unit time (e.g., 1 minute) - Fatigue alerts are triggered if frequency falls below a preset threshold (e.g., 15 blinks/minute) or prolonged eye closure is detected This solution demonstrates strong extensibility, with potential future integration of features like PERCLOS (Percentage of Eye Closure) or head pose analysis to improve detection accuracy. During actual deployment, attention should be paid to lighting conditions, with recommendations to incorporate histogram equalization or Retinex algorithms for image quality enhancement.