Uncertain label correction via auxiliary action unit graphs for facial expression recognition
Liu, Yang; Zhang, Xingming; Kauttonen, Janne; Zhao, Guoying (2022-11-29)
Y. Liu, X. Zhang, J. Kauttonen and G. Zhao, "Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition," 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 777-783, doi: 10.1109/ICPR56361.2022.9956650
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2023040635345
Tiivistelmä
Abstract
High-quality annotated images are significant to deep facial expression recognition (FER) methods. However, uncertain labels, mostly existing in large-scale public datasets, often mislead the training process. In this paper, we achieve uncertain label correction of facial expressions using auxiliary action unit (AU) graphs, called ULC-AG. Specifically, a weighted regularization module is introduced to highlight valid samples and suppress category imbalance in every batch. Based on the latent dependency between emotions and AUs, an auxiliary branch using graph convolutional layers is added to extract the semantic information from graph topologies. Finally, a re-labeling strategy corrects the ambiguous annotations by comparing their feature similarities with semantic templates. Experiments show that our ULC-AG achieves 89.31% and 61.57% accuracy on RAF-DB and AffectNet datasets, respectively, outperform the baseline and state-of-the-art methods.
Kokoelmat
- Avoin saatavuus [31941]