Driver drowsiness is a frequent cause of traffic accidents. Research on driver drowsiness detection methods is important to improve road traffic safety. Previous driving fatigue detection methods frequently extracted single features such as eye or mouth changes and trained shallow classifiers, which limit the generalisation capability of these methods. This study proposes a framework for recognising driver drowsiness expression by using facial dynamic fusion information and a deep belief network (DBN) to address the aforementioned problem. First, the landmarks and textures of the facial region are extracted from videos captured using a high‐definition camera. Then, a DBN is built to classify facial drowsiness expressions. Finally, the authors’ method is tested on a driver drowsiness dataset, which includes different genders, ages, head poses and illuminations. Certain experiments are also carried out to investigate the effects of different facial subregions and temporal resolutions on the accuracy of driver fatigue recognition. Results demonstrate the validity of the proposed method, which has an average accuracy of 96.7%.
Driver drowsiness detection using facial dynamic fusion information and a DBN
IET Intelligent Transport Systems ; 12 , 2 ; 127-133
2018-03-01
7 pages
Article (Journal)
Electronic Resource
English
deep belief network , temporal resolutions , cameras , driver information systems , driver drowsiness expression recognition , road safety , object detection , road traffic , traffic accidents , face recognition , driver fatigue recognition , road traffic safety improvement , video signal processing , illuminations , ages , head poses , emotion recognition , genders , driver drowsiness detection methods , texture extraction , DBN , facial drowsiness expression classification , belief networks , driver drowsiness dataset , facial subregions , feature extraction , facial dynamic fusion information , image fusion , high‐definition camera , landmark extraction
Driver drowsiness detection using facial dynamic fusion information and a DBN
IET | 2017
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