Eye Blink Detection for Smart Glasses

Abstract

Eye blink is a quick action of closing and opening of the eyelids. Eye blink detection has a wide range of applications in human computer interaction and human vision health care research. Existing approaches to eye blink detection often cannot suit well resource-limited eye blink detection platforms like Smart Glasses, which have limited energy supply and typically cannot afford strong imaging and computational capabilities. In this paper, we present an efficient and robust eye blink detection method for Smart Glasses. Our method first employs an eigen-eye approach to detect closing-eye in individual video frames. Our method then learns eye blink patterns based on the closing-eye detection results and detects eye blinks using a Gradient Boosting method. Our method further uses a non-maximum suppression algorithm to remove repeated detection of the same eye-blink action among consecutive video frames. Experiments with our prototyped smart glasses equipped with a low-power camera and an embedded processor show an accurate detection result (with more than 96% accuracy) on video frames of a small size of 16 × 12 at 96 fps, which enables a number of applications in health care, driving safety, and human computer interaction.