Deep Learning for Eye Blink Detection Implemented at the Edge

Abstract

Driver drowsiness is one of the major causes of accidents and fatal road crashes, causing a high human and economic cost. Recently, automatic drowsiness detection has begun to be recognized as a promising solution, receiving growing attention from industry and academics. In this letter, we propose to embed a convolutional neural network (CNN)-based solution in smart connected glasses to detect eye blinks and use them to estimate the driver's drowsiness level. This innovative solution is compared with a more traditional method, based on a detection threshold mechanism. The performance, battery lifetime, and memory footprint of both solutions are assessed for embedded implementation in the connected glasses. The results demonstrate that CNN outperforms the accuracy obtained by the threshold-based algorithm by more than 7%. Moreover, increased overheads in terms of memory and battery lifetime are acceptable, thus making CNN a viable solution for drowsiness detection in wearable devices.