Facial Expression Recognition Based on Multi-feature Fusion

For the insufficient generalization ability of lightweight facial expression recognition algorithms, a facial expression recognition method based on multi-feature fusion and attention mechanism is proposed. LBP is used to reduce the interference of meaningless information of original face image, the dual-branch neural network extracts the features of original face image and LBP image respectively, merges the middle-level and high-level feature information extracted by these two networks and strengthens important features through attention mechanism for facial expression recognition, which can generate a large amount of discriminative features by multi-feature fusion to improve the recognition performance with a small number of parameters. The experimental results show that the recognition rate of this method has reached 70.21% on Fer2013 dataset and 95.59% on CK+ dataset respectively, which effectively improves the performance of lightweight expression recognition algorithm.