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

**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**


Received: 22 February 2019; Accepted: 26 March 2019; Published: 29 March 2019

**Abstract:** 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 di fferent local regions, namely the eyes, nose, and mouth. By using an SE block, the network automatically allocated weights to di fferent 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.

**Keywords:** NIR facial expression recognition; SE block; 3D CNN; adaptive feature weights calibration
