Research on masked face recognition by fusing multi-level features of PVT
The prevalence of respiratory diseases has made masks play an important role,which has brought new challenges to face rec-ognition algorithms.Inspired by the multi-scale feature fusion model,a Pyramid Vision Transformer(PVT)based face mask feature extraction model is proposed.The model introduces self-attention mechanism to extract rich face information,and realizes multi-scale attention to mask faces by fusing multi-level feature vectors of PVT.Compared with traditional feature fusion model,the model has higher recognition accuracy and fewer parameters.In addition,the model adopts Sub-center ArcFace loss function to improve robust-ness.The model was trained on a large scale simulated mask face dataset,and tested and evaluated on ordinary face,simulated mask face and real mask face dataset respectively.The experimental results show that the proposed method has higher recognition accuracy than other mainstream methods,and is an effective mask face recognition method.
masked face recognitionTransformerself-attentionfeature fusion
NETL
NSTL
万方数据
