Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition

Gong Wenjuan*, Yue Zhang, Wei Wang, Peng Cheng, Jordi Gonzalez Sabate

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review


<jats:p>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</jats:p>
Original languageEnglish
JournalACM Transactions on Multimedia Computing, Communications and Applications
Publication statusPublished - 2 Jun 2022


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