Meta-Auxiliary Learning for Micro-Expression Recognition

TL;DR

This paper introduces LightmanNet, a dual-branch meta-auxiliary learning method that enhances micro-expression recognition by addressing data scarcity, feature subtlety, and individual differences, achieving superior robustness and efficiency.

Contribution

The paper proposes a novel dual-branch meta-auxiliary learning framework, LightmanNet, for micro-expression recognition that effectively leverages limited data and auxiliary tasks to improve generalization.

Findings

LightmanNet outperforms existing methods on benchmark datasets.

The dual-branch bi-level optimization enhances feature discrimination.

The approach improves robustness and efficiency in micro-expression recognition.

Abstract

Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, despite its wide applications in various scenarios, micro-expression recognition (MER) remains a challenging problem in real life due to three reasons, including (i) data-level: lack of data and imbalanced classes, (ii) feature-level: subtle, rapid changing, and complex features of MEs, and (iii) decision-making-level: impact of individual differences. To address these issues, we propose a dual-branch meta-auxiliary learning method, called LightmanNet, for fast and robust micro-expression recognition. Specifically, LightmanNet learns general MER knowledge from limited data through a dual-branch bi-level optimization process: (i) In the first level, it obtains task-specific MER knowledge by learning in two branches, where the first branch is for learning MER features via primary MER tasks, while the other branch is for guiding the model obtain discriminative features via auxiliary tasks, i.e., image alignment between micro-expressions and macro-expressions since their resemblance in both spatial and temporal behavioral patterns. The two branches of learning jointly constrain the model of learning meaningful task-specific MER knowledge while avoiding learning noise or superficial connections between MEs and emotions that may damage its generalization ability. (ii) In the second level, LightmanNet further refines the learned task-specific knowledge, improving model generalization and efficiency. Extensive experiments on various benchmark datasets demonstrate the superior robustness and efficiency of LightmanNet.