Real-Time Face Eye Tracking and Blink Detection using Event Cameras [article]

Cian Ryan, Brian O Sullivan, Amr Elrasad, Joe Lemley, Paul Kielty, Christoph Posch, Etienne Perot
2020 arXiv   pre-print
Event cameras contain emerging, neuromorphic vision sensors that capture local light intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame based cameras and offers several significant advantages: low energy consumption, high temporal resolution, high dynamic range and low latency. Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers
more » ... ical and cognitive state. Event cameras are particularly suited to DMS due to their inherent advantages. This paper proposes a novel method to simultaneously detect and track faces and eyes for driver monitoring. A unique, fully convolutional recurrent neural network architecture is presented. To train this network, a synthetic event-based dataset is simulated with accurate bounding box annotations, called Neuromorphic HELEN. Additionally, a method to detect and analyse drivers eye blinks is proposed, exploiting the high temporal resolution of event cameras. Behaviour of blinking provides greater insights into a driver level of fatigue or drowsiness. We show that blinks have a unique temporal signature that can be better captured by event cameras.
arXiv:2010.08278v1 fatcat:gr2yzww6hnajflmisb6onhpbw4