Real-Time Driver-Drowsiness Detection System Using Facial Features

Wanghua Deng · Ruoxue Wu

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Sleep and Work-Related Fatigue Human-Automation Interaction and Safety Face recognition and analysis

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.

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References

2012Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness 2015Driver Behavior Analysis for Safe Driving: A Survey 2014YawDD

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2022A Review of Recent Developments in Driver Drowsiness Detection Systems 2023Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review 2021A Survey on Driver Behavior Analysis From In-Vehicle Cameras 2020Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network 2025Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach 2021Estimation of Driver Vigilance Status Using Real-Time Facial Expression and Deep Learning 2022Driver Drowsiness Prediction Based on Multiple Aspects Using Image Processing Techniques 2023Real-time driver fatigue detection system with deep learning on a low-cost embedded system

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