Real-time eye blink detection using general cameras: a facial landmarks approach

Eyes are essential in Human-Computer Interaction (HCI) as they provide valuable insights into a person's thoughts and intentions. However, current eye movement analysis methods require specialized equipment or high-quality videos, making them less accessible and usable. This paper proposes a real-time eye blink detection algorithm that uses standard cameras, making it widely applicable and convenient. The approach achieves accurate and efficient eye blink detection by leveraging the Eye Aspect Ratio (EAR) algorithm and facial landmarks technique. This paper developed a Python application and conducted tests using a laptop webcam to validate the performance and practicality of the algorithm across various settings. The algorithm's effectiveness depends on carefully tuning parameters such as threshold and frame rate. The results demonstrate the algorithm's potential in real-time eye blink detection, with potential applications in drowsiness detection during driving, prevention of Computer Vision Syndrome, and assistance for individuals with disabilities. By enabling eye blink detection using commonly available cameras, the algorithm paves the way for integrating eye movement analysis into everyday devices and systems, enhancing user experience and enabling more natural interactions. Further refinement and optimization of this approach hold promise for a wide range of applications in HCI, healthcare, and beyond, opening up new possibilities for research and innovation.