首页|Expression Complementary Disentanglement Network for Facial Expression Recognition

Expression Complementary Disentanglement Network for Facial Expression Recognition

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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 re-construction 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,in-ducing 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 com-pleteness and exactness of the FED task.Experimental results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.

Facial expression recognitionFacial expression disentanglementFace reconstructionExpression incentiveExpression inhibition

Shanmin WANG、Hui SHUAI、Lei ZHU、Qingshan LIU

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College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

College of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China

School of Information and Control,Nanjing University of Information Science and Technology,Nanjing 210044,China

National Key Research and Development ProgramNational Natural Science Foundation of ChinaNational Science Foundation of Jiangsu Province

2022YFC240560061825601BK20192004B

2024

10.23919/cje.2022.00.351

电子学报(英文)

电子学报(英文)

CSTPCDEI
ISSN：1022-4653
年,卷(期)：2024.33(3)