Yawning Detection for Monitoring Driver Fatigue

Xiao Fan · Baocai Yin · Yanfeng Sun

112 citations0/0 files storedInternational Conference on Machine Learning and Cybernetics

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Sleep and Work-Related Fatigue Face recognition and analysis Face and Expression Recognition

Abstract

Fatigue driving is an important reason of traffic accidents. Yawning is an evidence of driver fatigue. This paper proposes to locate and track a driver's mouth movement using a CCD camera to study on monitoring and recognizing a driver's yawning. Firstly detecting drivers' faces uses Gravity-Center template, then detecting drivers' left and right mouth corners by grey projection, and extracting texture features of drivers' mouth corners (left and right) using Gabor wavelets. Finally LDA is applied to classify feature vectors to detect yawning. The method is tested on 400 images from twenty videos. In contrast, yawning is also detected by the ratio of mouth height and width. The experiment results show that Gabor coefficients are more powerful than geometric features to detect yawning and the average recognition rate is 95% which has more than 20% improvement.

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References

2004Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue 2006A probabilistic framework for modeling and real-time monitoring human fatigue 2005Monitoring mouth movement for driver fatigue or distraction with one camera 2005Drowsy Driver Detection and Warning System for Commercial Vehicle Drivers: Field Operational Test Design, Data Analyses, and Progress

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2008Driver Fatigue Detection Using Mouth and Yawning Analysis 2020Vision-Based Fatigue Driving Recognition Method Integrating Heart Rate and Facial Features 2014Driver’s Fatigue Detection Based on Yawning Extraction 2013Pain detection through shape and appearance features 2022A review of driver fatigue detection and its advances on the use of RGB-D camera and deep learning 2021A comprehensive review of approaches to detect fatigue using machine learning techniques 2021A novel approach for driver fatigue detection based on visual characteristics analysis 2009Yawning detection for monitoring driver fatigue based on two cameras

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