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

被引：11

作者：

Chen, Ying ^{[1
,2
]}

Zhang, Zhihao ^{[1
,2
]}

Zhong, Lei ^{[1
,2
]}

Chen, Tong ^{[1
,2
,3
]}

Chen, Juxiang ^{[1
,2
]}

Yu, Yeda ^{[1
,2
]}

机构：

[1] Southwest Univ, Chongqing Key Lab Nonlinear Circuit & Intelligent, Chongqing 400715, Peoples R China

[2] Chinese Acad Sci, Chongqing Key Lab Artificial Intelligence & Serv, Chongqing Inst Green & Intelligent Technol, Chongqing 400715, Peoples R China

[3] Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China

来源：

ELECTRONICS | 2019年 / 8卷 / 04期

基金：

中国国家自然科学基金;

关键词：

NIR facial expression recognition; SE block; 3D CNN; adaptive feature weights calibration; SYSTEM; MODEL; 3D;

D O I：

10.3390/electronics8040385

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

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