Facial expression optimal class separability for expression recognition
Xun Huang, Jinfan Lıang · 2023
Facial Expression Recognition (FER) is a hard work, but in the current research on expression recognition technique, the regression of recognition performance is caused by misclassification problem which is caused by non-saliency of local features for facial image. In this paper, we introduce a framework for FER using inter-class separability for regional-feature fusion based on multi-domain expression generation (GSER).We fuse the regional features from multiple layers, and the local feature from most informative regions is enhanced to improve the precision of expression classification. An inter-class separability method based on pre-defined evenly-distributed class centroids is discussed for loss function, which solve the misclassification problem of expressions. The framework is evaluated over different standard facial expression datasets including KDEF and RAF-DB, and experiments show that the problems for unbalance of sample size and misclassification are solved, the robustness and effectiveness of expression classification is improved.