A Real-Time Eye Tracking System for the Detection of Eye Blinks.

Introduction: This paper proposes an eye blink detection system that automatically detects eye blinks, which can be an indicator of fatigue or cognitive load, among others. As a key feature, the real-time capability of the system is being required to use it, for example, as a monitoring system for people in potentially critical situations (e.g., drivers or operators of heavy machinery).

Methods: The system uses the Viola-Jones algorithm for face detection and the median flow tracker to track the face in video sequences. Eye detection is implemented using face proportions, and template matching is used for blink detection.

Results: The resulting system processes 40-47 frames per second on default consumer hardware and achieves an accuracy of 80.33% and a precision of 85.22% in the evaluation.

Discussion: The proposed system shows promising results under ideal viewing conditions but has difficulty maintaining high precision during head movements. The proposed system could be integrated with various health-related assistance systems to monitor the individual's well-being in real time, as long as their head is observed from the front if possible.