얼굴 랜드마크 기반 운전자 모니터링 시스템
Driver Monitoring System Based on Facial Landmarks
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We propose a lightweight algorithm for detecting driver drowsiness by analyzing eye blinking patterns, eye closure duration, and head tilt using the Eye Aspect Ratio (EAR) combined with average central point detection. While EAR effectively detects drowsiness through sustained low values, it can result in false positives during activities such as talking or using a smartphone. To address this, we incorporated gaze stability analysis by comparing the recent average central point of the face with its current position to determine forward attention, enhancing accuracy and reliability in detecting both drowsiness and inattentive behaviors. This approach, combined with Google’s MediaPipe Mesh model—which extracts 468 facial landmarks and operates efficiently on mobile and low-spec hardware—ensures stable and highly accurate detection. Built on the BlazeFace framework with an average precision of 98.61%, this system provides a practical, resource-efficient solution for real-world driver monitoring without the need for high-performance computing.
Abstract
We propose a lightweight algorithm for detecting driver drowsiness by analyzing eye blinking patterns, eye closure duration, and head tilt using the Eye Aspect Ratio (EAR) combined with average central point detection. While EAR effectively detects drowsiness through sustained low values, it can result in false positives during activities such as talking or using a smartphone. To address this, we incorporated gaze stability analysis by comparing the recent average central point of the face with its current position to determine forward attention, enhancing accuracy and reliability in detecting both drowsiness and inattentive behaviors. This approach, combined with Google’s MediaPipe Mesh model—which extracts 468 facial landmarks and operates efficiently on mobile and low-spec hardware—ensures stable and highly accurate detection. Built on the BlazeFace framework with an average precision of 98.61%, this system provides a practical, resource-efficient solution for real-world driver monitoring without the need for high-performance computing.