四川大学学报（自然科学版）2024，Vol.61Issue(1)：61-68,8.DOI:10.19907/j.0490-6756.2024.012002
基于多层次特征融合的Transformer人脸识别方法
Transformer face recognition method based on multi-level feature fusion
摘要
Abstract
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.关键词
人脸识别/Transformer/多尺度特征/特征融合Key words
Face recognition/Transformer/Multi-scale features/Feature fusion分类
信息技术与安全科学引用本文复制引用
夏桂书,朱姿翰,魏永超,朱泓超,徐未其..基于多层次特征融合的Transformer人脸识别方法[J].四川大学学报（自然科学版）,2024,61(1):61-68,8.基金项目
西藏科技厅重点研发计划(XZ202101ZY0017G) （XZ202101ZY0017G）
四川省科技厅重点研发项目(2022YFG0356) （2022YFG0356）
中国民用航空飞行学院科研基金(J2020-126,J2020-040,J2021-056) （J2020-126,J2020-040,J2021-056）
