关键词: 多视角人脸表情识别, MVFE-LightNet, 残差网络, 深度可分离卷积, 压缩和奖惩网络模块, 空间金字塔池化

Abstract: Facial expression recognition is attracting growing interest in the field of computer vision. A multi-view facial expression recognition method based on Multi-View Facial Expression Lightweight Network（MVFE-LightNet） is proposed to slove some existing problems, such as multi-view facial change and facial information loss in the natural state. Firstly, the convolutional network is designed to extract the facial expression features from different perspectives based on the residual network, and depthwise separable convolution are introduced to reduce the network parameters. Secondly, Sequeeze-and-Excitation block is embedded to learn feature weights, using feature re-calibration to improve network representation, and the robustness of the network is enhanced by adding spatial pyramid pooling. Finally, for further optimizing the recognition results, the Adam with weight decay optimization method is used to accelerate the convergence of the network model. Experiments on RaFD, BU-3DFE and Fer2013 expression database show that the method has state-of-the-art classification accuracy and reduces network computing time.

Key words: multi-view facial expression recognition, Multi-View Facial Expression Lightweight Network（MVFE-LightNet）, residual network, depthwise separable convolution, Sequeeze-and-Excitation block, spatial pyramid pooling