FaceCaps for facial expression recognition

Fangyu Wu, Chaoyi Pang, Bailing Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Article numbere2021
JournalComputer Animation and Virtual Worlds
Issue number3-4
Publication statusPublished - 2 Jun 2021
Externally publishedYes


  • capsule network
  • facial expression recognition
  • feature embedding


Dive into the research topics of 'FaceCaps for facial expression recognition'. Together they form a unique fingerprint.

Cite this