A Novel Facial Expression Recognition Method Based on AMSSD Model
Qiuchen Wang, Xiaowei Xu, Tao Ye, Xiaodong Wang, Fang-Fang Chen, Shanping Zhang · 2021
Facial expression recognition is a technology that uses computers to understand human emotions to provide a good human-computer interaction experience. To further improve the accuracy and efficiency of facial expression recognition, we propose a lightweight model AMSSD based on SSD reconstruction. In this paper, we use the MobileNetV2 lightweight network to replace the SSD base net to reduce the amount of parameters. And use CBAM to improve feature extraction capabilities from the two dimensions of channel and space. This method reduces the number of parameters and improves the efficiency and accuracy of recognition. The AMSDD model achieves 0.733 mAP on the FER2013 facial expression data set, which fully verified the effectiveness of the method.