Research Article
Multi-feature Fusion Network Acts on Facial Expression Recognition
@INPROCEEDINGS{10.1007/978-3-031-04245-4_33, author={Jingyu Li and Weiyue Cheng and Jiahao Geng and Kezheng Lin}, title={Multi-feature Fusion Network Acts on Facial Expression Recognition}, proceedings={6GN for Future Wireless Networks. 4th EAI International Conference, 6GN 2021, Huizhou, China, October 30--31, 2021, Proceedings}, proceedings_a={6GN}, year={2022}, month={5}, keywords={Facial expression recognition CNN Feature fusion LBP}, doi={10.1007/978-3-031-04245-4_33} }- Jingyu Li
Weiyue Cheng
Jiahao Geng
Kezheng Lin
Year: 2022
Multi-feature Fusion Network Acts on Facial Expression Recognition
6GN
Springer
DOI: 10.1007/978-3-031-04245-4_33
Abstract
In order to solve the problem of single-channel convolutional neural network feature loss in the process of facial expression recognition, a facial expression recognition algorithm based on multi-feature fusion network is proposed. The algorithm uses the dual-channel convolutional neural network model DCNN-FER (Dual-channel Convolutional Neural Network Model for Facial Expression Recognition). The pre-processed face image is input to channel one to obtain global features, and the face image that has been processed by Local Binary Patterns (LBP) is input to channel two to obtain local texture features. At the same time, it is used in part of the convolutional layer. The Convolutional Block Attention Module (CBAM) enhances the network’s focus on the useful information of the image and suppresses useless features. Finally, new features are formed by weighted fusion and sent to the softmax layer for classification. This algorithm not only considers the extraction of overall facial features, but also enriches local texture features. Compared with other methods on the FER2013 and CK + facial expression data sets, the method in this paper shows good robustness.
