Deep Global Multiple-Scale and Local Patches Attention Dual-Branch Network for Pose-Invariant Facial Expression Recognition  

作　　者：Chaoji Liu Xingqiao Liu Chong Chen Kang Zhou 

机构地区：[1]School of Electrical and Information Engineering,Jiangsu University,Zhenjiang,212013,China [2]School of Electrical Engineering,Yancheng Institute of Technology,Yancheng,224051,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第4期405-440,共36页工程与科学中的计算机建模（英文）

基　　金：supported by the National Natural Science Foundation of China (No.31872399);Advantage Discipline Construction Project (PAPD,No.6-2018)of Jiangsu University。

摘　　要：Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.

关 键 词：Pose-invariant FER global multiple-scale(GMS) local patches attention(LPA) model-level fusion 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]