Driver monitoring systems (DMS) are a key component of vehicular safety and
essential for the transition from semiautonomous to fully autonomous driving. A
key task for DMS is to ascertain the cognitive state of a driver and to
determine their level of tiredness. Neuromorphic vision systems, based on event
camera technology, provide advanced sensing of facial characteristics, in
particular the behavior of a driver's eyes. This research explores the
potential to extend neuromorphic sensing techniques to analyze the entire
facial region, detecting yawning behaviors that give a complimentary indicator
of tiredness. A neuromorphic dataset is constructed from 952 video clips (481
yawns, 471 not-yawns) captured with an RGB color camera, with 37 subjects. A
total of 95200 neuromorphic image frames are generated from this video data
using a video-to-event converter. From these data 21 subjects were selected to
provide a training dataset, 8 subjects were used for validation data, and the
remaining 8 subjects were reserved for an "unseen" test dataset. An additional
12300 frames were generated from event simulations of a public dataset to test
against other methods. A CNN with self-attention and a recurrent head was
designed, trained, and tested with these data. Respective precision and recall
scores of 95.9 percent and 94.7 percent were achieved on our test set, and 89.9
percent and 91 percent on the simulated public test set, demonstrating the
feasibility to add yawn detection as a sensing component of a neuromorphic DMS.

本研究探索了将类脑视觉技术扩展到分析整个面部区域的潜力，以检测疲劳的补充指标 —— 打哈欠行为，并使用新型事件相机技术构建了基于类脑神经元的数据集，从而设计、培训和测试了一个用于检测打哈欠的具有自我关注和循环头的卷积神经网络。