A Deep Neural Network for Real-Time Driver Drowsiness Detection

Toan H. Vu · An Dang · Jia‐Ching Wang

42 citations0/0 files storedIEICE Transactions on Information and Systems

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Sleep and Work-Related Fatigue Advanced Neural Network Applications Face recognition and analysis

Abstract

We develop a deep neural network (DNN) for detecting driver drowsiness in videos. The proposed DNN model that receives driver's faces extracted from video frames as inputs consists of three components - a convolutional neural network (CNN), a convolutional control gate-based recurrent neural network (ConvCGRNN), and a voting layer. The CNN is to learn facial representations from global faces which are then fed to the ConvCGRNN to learn their temporal dependencies. The voting layer works like an ensemble of many sub-classifiers to predict drowsiness state. Experimental results on the NTHU-DDD dataset show that our model not only achieve a competitive accuracy of 84.81% without any post-processing but it can work in real-time with a high speed of about 100 fps.

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2016Training deep networks for facial expression recognition with crowd-sourced label distribution 2017Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network 2017Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks 2012Prevalence of motor vehicle crashes involving drowsy drivers, United States, 1999–2008 2017Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety 2019Driver Monitoring Using Sparse Representation With Part-Based Temporal Face Descriptors 2017MSTN: Multistage Spatial-Temporal Network for Driver Drowsiness Detection 2017Representation Learning, Scene Understanding, and Feature Fusion for Drowsiness Detection

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