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Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition
Chen, Ying1,2; Zhang, Zhihao1,2; Zhong, Lei1,2; Chen, Tong1,2,3; Chen, Juxiang1,2; Yu, Yeda1,2
2019-04-01
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.
关键词NIR facial expression recognition SE block 3D CNN adaptive feature weights calibration
DOI10.3390/electronics8040385
发表期刊ELECTRONICS
ISSN2079-9292
卷号8期号:4页码:15
通讯作者Chen, Tong(c_tong@swu.edu.cn)
收录类别SCI
WOS记录号WOS:000467751100016
语种英语