Multi-level Feature Fusion Facial Expression Recognition Network

In order to solve the problems of face features extraction and the long time consuming in network training, we propose a facial expression recognition network based on multi-level feature fusion structure. The network structure consists of the following three parts: The multi-level feature fusion block; The multi-granularity feature extraction unit; The global feature fusion structure composed of global residual connection. We evaluated our proposed algorithm on the CK+ dataset and FER2013 dataset, and finally achieved 94.07% accuracy and 65.4% accuracy respectively. Experimental results show that our algorithm can effectively improve the accuracy of expression recognition tasks.