Self curing facial expression recognition based on multi-attention mechanism

Facial expression recognition technology has important application value and broad application prospects in social life, criminal detectives and other fields. Aiming at the problem of insufficient expression feature extraction in the existing methods, which makes high-dimensional features easy to lose local key information; And the ambiguity of expressions in complex backgrounds leads to weak network generalization. In order to solve these problems, a self curing network under multi-attention mechanism (MASCNet) is proposed. The network will generate multi-scale features with attention weights, and by fusing features of different scales, the ability of the network model to represent local key information at a fine-grained level is improved. The self-attention mechanism module can assign importance weights to the fused features, constrain the proportion of uncertain samples in network training, and improve the generalization ability of the network. The highest recognition accuracy rates of this method on the FER2013 and RAF-DB datasets are 74.21% and 88.74% respectively. Experimental results show that this method can effectively recognize facial expressions and is superior to the existing mainstream methods such as MHBP and AHBRPN.