Real-Time Face & Eye Tracking and Blink Detection using Event Cameras

TL;DR

This paper introduces a novel neural network-based approach using event cameras for real-time face, eye tracking, and blink detection to improve driver monitoring systems with high temporal resolution and low latency.

Contribution

It presents a new fully convolutional recurrent neural network architecture and a synthetic event-based dataset for accurate face and eye tracking, along with a method for blink analysis.

Findings

Effective face and eye tracking with event cameras.

High accuracy in blink detection and analysis.

Potential for improved driver fatigue monitoring.

Abstract

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 physical 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.