关键词: 金字塔卷积, 面部特征, 注意力, 深度可分离混洗

Abstract: To address the problems of insufficient extraction ability and slow computation speed of facial expression features by traditional convolutional neural networks, a pyramidal convolutional model with multi-scale fused attention is proposed in this paper. In order to reduce the number of parameters of the network, improve the computational speed of the network, and increase the perceptual field of the model, the pyramidal convolutional structure is improved. In order to represent facial expression features from multiple scales and improve the ability of the model to represent facial features, the SECA coordinate attention module is proposed. In order to save the computational power of the network, solve the problem of model redundancy, and promote the fusion of information between channels, the depth-separable blending method is proposed. The experimental results show that the accuracy of the model is 72.89%, 98.55% and 94.37% on the public datasets FER2013, CK+ and JAFFE, respectively, with the number of parameters of 1.958×107. In comparison with other networks, the proposed network has better recognition and higher accuracy, while maintaining a faster computational speed.

Key words: pyramidal convolution, facial features, attention, depth-separable shuffle