Camera-based eye blink detection algorithm for assessing driver drowsiness

This paper presents an adaptive camera-based eye blink detection algorithm for assessing the level of drowsiness during driving. The data used in this study were collected from driving simulator experiments using a remote camera. Eye blink detection in the automotive context and for different driver states typically encounters some difficulties. It may be challenging to reliably distinguish between eye blink events and gaze-related eyelid closures, particularly the glances at the dashboard, since both exhibit a similar eyelid movement pattern. In addition, it is difficult to find comparable thresholds due to high inter-individual differences in the palpebral aperture. Furthermore, the blinking behavior is impacted by drowsiness and the blink patterns vary widely, which requires an adaptive algorithm to deal with this intra-individual variability of the blinks. These challenges are considered in the design of the presented blink detection algorithm. This algorithm is based essentially on a threshold for the maximum velocity of the eyelids. This threshold is determined using k-means clustering (k=2)and updated every five minutes of the drive. The accuracy of the algorithm is evaluated based on video labeling. The detection rates demonstrate that the algorithm performs very reliably in both awake and drowsy phases of the driving experiments.