关键词: 多尺度表情识别, 改进卷积, 特征筛选, 浅层特征处理, 通道注意力机制, SMU激活函数

Abstract: In the expression recognition task, due to the diversity and uncertainty of facial features, it is easy to have problems such as missing features and low feature extraction rate in the feature extraction stage. At the same time, a large number of redundant features will be accumulated in the network training process with feature reuse structure, which will affect the feature quality. To solve the above problems, this paper proposes a residual multiscale feature fusion attentional network (RMFANet) based on feature filtering and improved convolution. Referring to the idea of blue print separable convolution and dilated convolution, the improved convolution is designed and introduced, so that the convolution can be separated more effectively and the efficiency of feature extraction can be improved. Based on the improved convolution model, a multi-scale parallel feature extraction path is designed and introduced to enrich the feature information. The feature screening module is designed and introduced to reduce the redundant features generated in the process of model training, screen out high-quality features and improve the quality of features. A shallow input feature processing layer is designed and introduced to simplify the network structure and reduce the computational complexity. Channel attention mechanism is introduced to highlight local key feature information. Finally, the SMU activation function is introduced to improve the nonlinear capability of the model. It can be seen from the experimental results that the model can achieve 70.298% and 96.566% recognition accuracy on Fer2013 data set and CK+ data set respectively on the premise of low parameter size and calculation cost, which has better robustness than the traditional algorithm.

Key words: multiscale expression recognition, improved convolution, feature filtering, shallow layer feature processing, channel attention mechanism, SMU activation function