Real-Time Driver-Drowsiness Detection System Using Facial Features

Wanghua Deng,Ruoxue Wu

doi:10.1109/access.2019.2936663

Abstract

The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. In this paper, we propose a system called DriCare, which detects the drivers’ fatigue status, such as yawning, blinking, and duration of eye closure, using video images, without equipping their bodies with devices. Owing to the shortcomings of previous algorithms, we introduce a new face-tracking algorithm to improve the tracking accuracy. Further, we designed a new detection method for facial regions based on 68 key points. Then we use these facial regions to evaluate the drivers’ state. By combining the features of the eyes and mouth, DriCare can alert the driver using a fatigue warning. The experimental results showed that DriCare achieved around 92% accuracy.

Highlights

In recent years, an increase in the demand for modern transportation necessitates a faster car-parc growth
In the subjective detection method, a driver must participate in the evaluation, which is associated with the driver’s subjective perceptions through steps such as self-questioning, The associate editor coordinating the review of this article and approving it for publication was Gustavo Olague
We introduce the multitask convolutional neural networks (MTCNN) [18] to compensate for the inability of the kernelized correlation filters (KCF) algorithm to mark the target in the first frame and prevent losing the target

Summary

INTRODUCTION

An increase in the demand for modern transportation necessitates a faster car-parc growth. We propose a non-contact method called DriCare to detect the level of the driver’s fatigue. Wu: Real-Time Driver-Drowsiness Detection System Using Facial Features driving, his or her head may be moving; tracking the trajectory of the head in time is important once the position of the head changes. According to Walter [12], the rate of the driver’s eye closure is associated with the degree of drowsiness Based on this principle, Grace et al [13] proposed PERCLOS (percentage of eyelid closure over the pupil over time) and introduced Copilot to measure the level of the driver’s fatigue. DriCare proposes three different criteria to evaluate the degree of the driver’s drowsiness: the blinking frequency, duration of the eyes closing, and yawning.

RELATED WORK

THE PRINCIPLE OF MC-KCF

CONCLUSION