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Open AccessArticle

Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition

by Ying Chen 1,2, Zhihao Zhang 1,2, Lei Zhong 1,2, Tong Chen 1,2,3,*, Juxiang Chen 1,2 and Yeda Yu 1,2
1
Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China
2
Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing 400715, China
3
Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(4), 385; https://doi.org/10.3390/electronics8040385
Received: 22 February 2019 / Revised: 23 March 2019 / Accepted: 26 March 2019 / Published: 29 March 2019
(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)
Near-infrared (NIR) facial expression recognition is resistant to illumination change. In this paper, we propose a three-stream three-dimensional convolution neural network with a squeeze-and-excitation (SE) block for NIR facial expression recognition. We fed each stream with different local regions, namely the eyes, nose, and mouth. By using an SE block, the network automatically allocated weights to different local features to further improve recognition accuracy. The experimental results on the Oulu-CASIA NIR facial expression database showed that the proposed method has a higher recognition rate than some state-of-the-art algorithms. View Full-Text
Keywords: NIR facial expression recognition; SE block; 3D CNN; adaptive feature weights calibration NIR facial expression recognition; SE block; 3D CNN; adaptive feature weights calibration
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Chen, Y.; Zhang, Z.; Zhong, L.; Chen, T.; Chen, J.; Yu, Y. Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition. Electronics 2019, 8, 385.

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