Eye Facial Gesture System Controller Based on AI ML

Abstract

Abstract: This paper presents a ground-breaking real-time algorithm for accurately detecting eye blinks in video sequences captured using standard cameras. The algorithm utilizes advanced landmark detectors trained on diverse datasets to ensure robustness against variations in head orientation, illumination, and facial expressions. By employing precise landmark detection techniques, the algorithm estimates the level of eye opening using the eye aspect ratio (EAR) in each frame. An SVM classifier analyses the patterns of EAR values within a short temporal window to identify eye blinks. Comparative evaluations on popular datasets demonstrate the superior performance of the proposed algorithm compared to existing methods. Additionally, the research explores the utilization of eye movements for controlling computer programs, evaluating four distinct approaches in a user-friendly photo viewer. The evaluation process considers various factors such as component sizes, execution time, unintended selections, and gesture repetitions. User experiments reveal that component sizes of 200px provide a convenient and efficient means of application control. The gaze-based method and gestures based on joining points receive positive feedback and exhibit satisfactory performance. This paper contributes significantly to the field of eye-based interaction by introducing an innovative blink detection algorithm and conducting a comprehensive evaluation of eye movement approaches for application control.