Expression Complementary Disentanglement Network for Facial Expression Recognition  

作　　者：Shanmin WANG Hui SHUAI Lei ZHU Qingshan LIU 

机构地区：[1]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]College of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China [3]School of Information and Control,Nanjing University of Information Science and Technology,Nanjing 210044,China

出　　处：《Chinese Journal of Electronics》2024年第3期742-752,共11页电子学报（英文版）

基　　金：supported by the National Key Research and Development Program (Grant No.2022YFC2405600);the National Natural Science Foundation of China (Grant No.61825601);the National Science Foundation of Jiangsu Province (Grant No.BK20192004B)。

摘　　要：Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression recognition.Previous methods only care about facial expression disentanglement(FED)itself,ignoring the negative effects of other facial attributes.Due to the annotations on limited facial attributes,it is difficult for existing FED solutions to disentangle all disturbance from the input face.To solve this issue,we propose an expression complementary disentanglement network(ECDNet).ECDNet proposes to finish the FED task during a face reconstruction process,so as to address all facial attributes during disentanglement.Different from traditional reconstruction models,ECDNet reconstructs face images by progressively generating and combining facial appearance and matching geometry.It designs the expression incentive(EIE)and expression inhibition(EIN)mechanisms,inducing the model to characterize the disentangled expression and complementary parts precisely.Facial geometry and appearance,generated in the reconstructed process,are dealt with to represent facial expressions and complementary parts,respectively.The combination of distinctive reconstruction model,EIE,and EIN mechanisms ensures the completeness and exactness of the FED task.Experimental results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.

关 键 词：Facial expression recognition Facial expression disentanglement Face reconstruction Expression incentive Expression inhibition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]