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Eye state recognition based on deep integrated neural network and transfer learning

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Abstract

Eye state recognition is widely used in many fields, such as driver drowsiness recognition, facial expression classification, and human–computer interface technology. This study proposes a novel framework based on the deep learning method to classify eye states in still facial images. The proposed method combines a deep neural network and a deep convolutional neural network to construct a deep integrated neural network for characterizing useful information in the eye region by use of the joint optimization method. A transfer learning strategy is applied to extract effective abstract eye features and improve the classification capability of the proposed model on small sample datasets. Experimental results on the Closed Eyes in the Wild (CEW) and Zhejiang University Eyeblink datasets show that the proposed approach outperforms other state-of-the-art methods. In addition, the effects of transfer learning methods with different pretraining datasets on classification accuracy are investigated with the CEW dataset. A driver drowsiness recognition dataset is constructed and used in an experiment to evaluate the effectiveness of the proposed method in driving environments. Experimental results demonstrate that the proposed method performs more stably and robustly than do other methods.

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Acknowledgments

This work was supported by the Open Foundation of State Key Laboratory of Automotive Simulation and Control (China, Grant no. 20161105).

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Authors and Affiliations

  1. School of Mechanical Engineering, Shandong University, Jinan, China

    Lei Zhao, Zengcai Wang, Guoxin Zhang, Yazhou Qi & Xiaojin Wang

  2. Key Laboratory of High-efficiency and Clean Mechanical Manufacture (Shandong University), Ministry of Education, Jinan, China

    Zengcai Wang

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  1. Lei Zhao
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  2. Zengcai Wang
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  3. Guoxin Zhang
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  4. Yazhou Qi
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  5. Xiaojin Wang
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Correspondence to Zengcai Wang.

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Additional information

In general, convolutional neural network is a type of neural network. However, a neural network is defined as a multi-layer perceptron using back propagation for training in this study to distinguish between the two models.

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Zhao, L., Wang, Z., Zhang, G. et al. Eye state recognition based on deep integrated neural network and transfer learning. Multimed Tools Appl 77, 19415–19438 (2018). https://doi.org/10.1007/s11042-017-5380-8

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