Robust facial expression recognition via lightweight reinforcement learning for rehabilitation robotics  

作　　者：CHEN Yifan FAN Weiming GAO Hongwei YU Jiahui JU Zhaojie 

机构地区：[1]School of Computing,University of Portsmouth,PO13HE,UK [2]School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,China [3]Department of Biomedical Engineering,Zhejiang University,Hangzhou 310027,China

出　　处：《Optoelectronics Letters》2025年第2期97-104,共8页光电子快报(英文版)

基　　金：supported by the National Natural Science Foundation of China (No.52075530);the AiBle Project Co-financed by the European Regional Development Fund;Liaoning Province Higher Education Innovative Talents Program Support Project (No.LR2019058);the Scientific Research Project of Liaoning Education Department (No.LG201909);the Liaoning Province Joint Open Fund for Key Scientific and Technological Innovation Bases (No.2021-KF-12-05);the Zhejiang Provincial Natural Science Foundation of China (No.LQ23F030001)。

摘　　要：This paper proposes a lightweight reinforcement network (LRN) and auxiliary label distribution learning (ALDL)based robust facial expression recognition (FER) method.Our designed representation reinforcement (RR) network mainly comprises two modules,i.e.,the RR module and the auxiliary label space construction (ALSC) module.The RR module highlights key feature messaging nodes in feature maps,and ALSC allows multiple labels with different intensities to be linked to one expression.Therefore,LRN has a more robust feature extraction capability when model parameters are greatly reduced,and ALDL is proposed to contribute to the training effect of LRN in the condition of ambiguous training data.We tested our method on FER-Plus and RAF-DB datasets,and the experiment demonstrates the feasibility of our method in practice during rehabilitation robots.

关 键 词：REHABILITATION LIGHTWEIGHT AUXILIARY 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程] R496[医药卫生—康复医学]