%0 Journal Article
%T Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition
%A Wenjuan Gong
%A Zhang Yue
%A Wei Wang
%A Cheng Peng
%A Jordi Gonzalez
%J ACM Transactions on Multimedia Computing, Communications, and Applications
%D 2022
%F Wenjuan Gong2022
%O ISE; 600.157
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3692), last updated on Tue, 25 Apr 2023 15:36:06 +0200
%X Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.
%K Feature Fusion
%K Model Fusion
%K Meta-Learning
%K Micro-Expression Recognition
%U http://dx.doi.org/10.1145/3539576