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Journal of Electronic Imaging

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning
Author(s): Lei Zhao; Zengcai Wang; Xiaojin Wang; Yazhou Qi; Qing Liu; Guoxin Zhang
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Paper Abstract

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Paper Details

Date Published: 6 October 2016
PDF: 11 pages
J. Electron. Imag. 25(5) 053024 doi: 10.1117/1.JEI.25.5.053024
Published in: Journal of Electronic Imaging Volume 25, Issue 5
Show Author Affiliations
Lei Zhao, Shandong Univ. (China)
Zengcai Wang, Shandong Univ. (China)
Xiaojin Wang, Shandong Univ. (China)
Yazhou Qi, Shandong Univ. (China)
Qing Liu, Shandong Univ. (China)
Guoxin Zhang, Shandong Univ. (China)

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