Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition-Reference-Cited by-同舟云学术

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

Published:2022-06-02 Issue: Volume: Page:
ISSN:1551-6857
Container-title:ACM Transactions on Multimedia Computing, Communications, and Applications
language:en
Short-container-title:ACM Trans. Multimedia Comput. Commun. Appl.

Author:

Gong Wenjuan,Zhang Yue¹,Wang Wei²,Cheng Peng³,Gonzàlez Jordi⁴

Affiliation:

1. China University of Petroleum (East China), China

2. Institute of Automation Chinese Academy of Sciences, China

3. Institute of High Performance Computing, A*STAR, Singapore

4. Computer Vision Center, Autonomous University of Barcelona, Spain

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Link

https://dl.acm.org/doi/pdf/10.1145/3539576

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