An Overview and Evaluation of Various Face and Eyes Detection Algorithms for Driver Fatigue Monitoring Systems

TL;DR

This paper reviews and evaluates various face and eyes detection algorithms for driver fatigue monitoring, identifying Viola-Jones and gradient-based eye center detection as the most effective methods for this application.

Contribution

It provides a comparative analysis of face and eyes detection algorithms specifically for driver fatigue systems, highlighting the most suitable methods for real-world deployment.

Findings

Viola-Jones face detector performs best for face detection

Gradient-based eye center detection is most appropriate for eyes detection

Potential for using eye behavior to estimate driver fatigue levels

Abstract

In this work various methods and algorithms for face and eyes detection are examined in order to decide which of them are applicable for use in a driver fatigue monitoring system. In the case of face detection the standard Viola-Jones face detector has shown best results, while the method of finding the eye centers by means of gradients has proven to be most appropriate in the case of eyes detection. The later method has also a potential for retrieving behavioral parameters needed for estimation of the level of driver fatigue. This possibility will be examined in future work.