Real-Time Eyes Monitoring System Using OpenCV Autonomous filtering using RNN

This paper introduces a novel Real-Time Eye Gazing and Monitoring System aimed at objectively and continuously assessing individuals across various environmental conditions. Addressing the challenge of accurate performance in diverse lighting conditions, the system employs Recurrent Neural Network (RNN) trained filters to enhance eye detection accuracy. Traditional methods often falter in non-ideal lighting, prompting the need for adaptive solutions. Through extensive experimentation, optimal filter combinations were identified, ensuring robustness across a spectrum of scenarios. Implementation via an OpenCV facilitates efficient processing without compromising detection accuracy. This paper achieved the list of main filters and their combinations which can be helpful to detect eyes in different environmental conditions.