基于多层次特征融合的Transformer人脸识别方法OA北大核心CSTPCD

The convolutional operation in a convolutional neural network only captures local information,whereas the Transformer retains more spatial information and can create long-range connections of ima-ges.In the application of vision field,Transformer lacks flexible image size and feature scale adaptation capability.To solve this problems,the flexibility of modeling at different scales is enhanced by using hi-erarchical networks,and a multi-scale feature fusion module is introduced to enrich feature information.This paper propose an improved Swin Face model based on the Swin Transformer model.The model u-ses the Swin Transformer as the backbone network and a multi-level feature fusion model is introduced to enhance the feature representation capability of the Swin Face model for human faces.a joint loss function optimisation strategy is used to design a face recognition classifier to realize face recognition.The experimental results show that,compared with various face recognition methods,the Swin Face recognition method achieves best results on LFW,CALFW,AgeDB-30,and CFP datasets by using a hi-erarchical feature fusion network,and also has good generalization and robustness.