Eyeblink Detection

Eyeblink detection research focuses on accurately identifying blinks in video, aiming to improve applications like fatigue monitoring and biometric security. Current efforts concentrate on developing robust methods for real-time, multi-person detection in unconstrained environments, often employing convolutional neural networks for efficient and accurate analysis of both RGB and near-infrared imagery. Large, publicly available datasets are increasingly crucial for training and benchmarking these algorithms, driving improvements in accuracy and speed, ultimately enhancing the reliability of applications that rely on subtle facial cues.