earticle

영어

This study systematically analyzes the impact of face angle and lighting changes on eye state recognition technology and compares the performance of three technologies: CNN, MediaPipe, and Dlib. Specifically, the CNN-based approach utilizes a transfer learning model, Inception, to assess eye state recognition accuracy. With recent advancements in AI and computer vision technology, eye state recognition has become crucial in applications like driver drowsiness detection, user authentication, and medical monitoring. However, the performance of these technologies is greatly influenced by face angle and lighting conditions. This research evaluates the recognition accuracy of the three technologies under various face angles and lighting conditions, finding that CNN demonstrates robust performance against both lighting and angle variations. This study aims to provide fundamental data to improve the reliability of eye state recognition technology and to suggest future research directions.