Meta-MMFNet : meta-learning based multi-model fusion network for micro-expression recognition
Gong, Wenjuan (China University of Petroleum)
Zhang, Yue (China University of Petroleum)
Wang, Wei (Chinese Academy of Sciences. Institute of Automation)
Cheng, Peng (A*STAR)
Gonzàlez, Jordi (Universitat Autònoma de Barcelona)

Date:	2023
Abstract:	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.
Grants:	Agencia Estatal de Investigación PID2020-120311RB-I00
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Language:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Subject:	Feature Fusion ; Model Fusion ; Meta-Learning ; Micro-Expression Recognition
Published in:	ACM transactions on multimedia computing, communications and applications, Vol. 20, issue 2 (February 2024) , art. 39, ISSN 1551-6865

DOI: 10.1145/3539576

Postprint 21 p, 1.8 MB

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