Facial expression optimal class separability for expression recognition

Xun Huang; Jinfan Liang

doi:10.1117/12.2684245

1 August 2023 Facial expression optimal class separability for expression recognition

Xun Huang, Jinfan Liang

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127543K (2023) https://doi.org/10.1117/12.2684245
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

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.

Citation Download Citation

Xun Huang and Jinfan Liang "Facial expression optimal class separability for expression recognition", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127543K (1 August 2023); https://doi.org/10.1117/12.2684245

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KEYWORDS

Facial recognition systems

Feature fusion

Convolution

Feature extraction

Image quality

Image fusion

Performance modeling

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