Face recognition method based on visual graph neural network
Currently,most face recognition methods rely on CNNs,which fuse local features through cascading to achieve fea-ture extraction,but ignore global semantic spatial information and are expensive to train.Compared with CNN,Transformer-based methods have fewer parameters and can effectively characterize global feature,but do not sufficiently characterize the relative spa-tial dependencies of each global region.To address these problems,a visual graph neural network approach for face recognition is proposed,introducing GCN as a backbone to capture the feature relationships and establish the dependencies of global feature re-gions;combined with the ECA module to improve the model's ability to perceive face features.In addition,based on Triplet Loss and Center Loss,a joint loss function is constructed as the objective function to constrain the intra-class features and improve the generalization ability of the model.Experimental results on LFW,CFP and AgeDB-30,show that the proposed method can achieve better performance,and the number of parameters and computational complexity are less.
face recognitionGNNspatial multi-scaleattention mechanisms
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