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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

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Real-time face & eye tracking and blink detection using event cameras.

Cian Ryan¹, Brian O'Sullivan¹, Amr Elrasad¹

¹Xperi Corporation, Galway, Ireland.

Neural Networks : the Official Journal of the International Neural Network Society

|April 19, 2021

Summary

This summary is machine-generated.

This study introduces event cameras for driver monitoring systems (DMS), enabling simultaneous face and eye detection/tracking. Their high temporal resolution uniquely captures blink patterns for fatigue detection.

Keywords:

Convolutional neural network Driver monitoring system Event cameras

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Area of Science:

Neuromorphic engineering
Computer vision
Automotive safety

Background:

Traditional frame-based cameras have limitations in dynamic environments.
Driver monitoring systems (DMS) are crucial for in-cabin safety.
Event cameras offer advantages like low latency and high dynamic range.

Purpose of the Study:

To propose a novel method for simultaneous face and eye detection and tracking using event cameras for DMS.
To develop a method for analyzing driver eye blinks to assess fatigue and drowsiness.
To leverage the unique capabilities of event cameras for enhanced driver monitoring.

Main Methods:

A novel, fully convolutional recurrent neural network architecture was designed.
A synthetic event-based dataset (Neuromorphic-HELEN) with bounding box annotations was simulated.
A method to detect and analyze driver eye blinks using event camera's high temporal resolution was developed.

Main Results:

The proposed method enables simultaneous detection and tracking of faces and eyes.
The high temporal resolution of event cameras allows for unique capture of blink temporal signatures.
Blink analysis provides insights into driver fatigue and drowsiness levels.

Conclusions:

Event cameras are well-suited for driver monitoring applications due to their inherent advantages.
The novel neural network architecture and blink analysis method show promise for improving DMS.
Exploiting event camera technology can lead to more effective fatigue and drowsiness detection systems.