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FaceCaps for facial expression recognition
Author(s) -
Wu Fangyu,
Pang Chaoyi,
Zhang Bailing
Publication year - 2021
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.2021
Subject(s) - computer science , regularization (linguistics) , facial expression recognition , facial expression , embedding , artificial intelligence , pattern recognition (psychology) , property (philosophy) , field (mathematics) , task (project management) , feature (linguistics) , computer vision , facial recognition system , philosophy , linguistics , mathematics , management , epistemology , pure mathematics , economics
Abstract Facial expression recognition (FER) is a significant research task in the computer vision field. In this paper, we present a novel network FaceCaps for facial expression recognition with the following novel characteristics: an embedding structure based on a Capsule network which encodes relative spatial relationships between features; incorporates the feature polymerization property of FaceNet, thus offering a more efficient approach to discriminate complex facial expressions; a target reconstruction loss as a better regularization term for Capsule networks. Experimental results on both lab‐controlled datasets (CK+) and real‐world databases (RAF‐DB and SFEW 2.0) demonstrate that the method significantly outperforms the state‐of‐the‐art.