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.
Original language | English |
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Article number | e2021 |
Journal | Computer Animation and Virtual Worlds |
Volume | 32 |
Issue number | 3-4 |
DOIs | |
Publication status | Published - 2 Jun 2021 |
Externally published | Yes |
Keywords
- capsule network
- facial expression recognition
- feature embedding