Deep convolutional neural network for drowsy student state detection. (28th February 2018)

• Record Type: Journal Article
• Authors: Zhao, Gang; Liu, Shan; Wang, Qi; Hu, Tao; Chen, Yawen; Lin, Luyu; Zhao, Dasheng
• Other Names: Barbosa Jorge G. guestEditor.; Jeannot Emmanuel guestEditor.; Li Maozhen guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 23(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-02-28
• Subjects: convolutional neural network -- deep learning -- eye state classification -- face detection -- student drowsiness; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4457
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Drowsy student state detection is helpful to understand the students' learning state, which is the necessary and basic aspect of teaching activities evaluation and assessment. The performance of traditional methods may deteriorate dramatically because of the external environment factors. In this paper, a novel drowsy student state detection method by integrating deep convolutional neural network is proposed at the first time in the literature. The proposed method avoids the complicated manual feature extraction operation and it can effectively reduce the interference of environmental factors in the application scenarios. Experimental results demonstrate that our approach can achieve high accuracy and lower error rate for drowsy student state detection. In addition, the results also show that our method outperforms traditional methods.