SFR-Net:sample-aware and feature refinement network for cross-domain micro-expression recognition

作     者：LIU Jing JI Xinyu WANG Mengmeng LIU Jing;JI Xinyu;WANG Mengmeng

作者机构：School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China Key Laboratory of Artificial IntelligenceMinistry of EducationShanghai 200240China 

出 版 物：《Optoelectronics Letters》 (光电子快报（英文版）)

年 卷 期：2023年第19卷第7期

页      面：437-442页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Key Laboratory of Artificial Intelligence Ministry of Education (No.AI2020006) 

主　　题：database Net refinement 

摘      要：Over the past several decades,micro-expression recognition(MER) has become a growing concern for scientific *** the filming conditions vary from database to database,previous single-domain MER methods generally exhibit severe performance drop when applied to another *** deal with this pressing problem,in this paper,a sample-aware and feature refinement network(SFR-Net) is proposed,which combines domain adaptation with deep metric learning to extract intrinsic features of micro-expressions for accurate *** the help of decoders,siamese networks increasingly refine shared features relevant to emotions while exclusive features irrelevant to emotions are gradually obtained by private *** order to achieve promising performance,we further design sample-aware loss to constrain the feature distribution in the high-dimensional feature *** results show the proposed algorithm can effectively mitigate the diversity among different micro-expression databases,and achieve better generalization performance compared with state-of-the-art methods.