Eye Detection

Eye detection research focuses on accurately locating and identifying eyes in images and videos, serving as a crucial preprocessing step for various applications like gaze tracking and facial recognition. Current research explores diverse approaches, including deep learning models such as Siamese networks and residual networks, as well as methods leveraging Gabor filters and near-infrared (NIR) imaging for improved robustness and speed. These advancements are impacting fields ranging from medical diagnosis (e.g., detecting Bell's Palsy) to biometric security (e.g., periocular recognition), offering more efficient and accurate solutions. The development of faster, more robust eye detection methods is continuously improving the performance of downstream applications.