Rethinking Key-frame-based Micro-expression Recognition: A Robust and Accurate Framework Against Key-frame Errors

TL;DR

This paper introduces CausalNet, a new framework for micro-expression recognition that is robust to key-frame index errors by analyzing entire sequences and focusing on muscle movement areas, outperforming existing methods.

Contribution

The paper presents CausalNet, which incorporates modules for locating muscle movements and modeling causal relationships, addressing key-frame errors in micro-expression recognition.

Findings

CausalNet achieves robust MER under key-frame noise.

It surpasses state-of-the-art methods on multiple benchmarks.

The approach maintains high accuracy with less reliance on precise key-frame annotations.

Abstract

Micro-expression recognition (MER) is a highly challenging task in affective computing. With the reduced-sized micro-expression (ME) input that contains key information based on key-frame indexes, key-frame-based methods have significantly improved the performance of MER. However, most of these methods focus on improving the performance with relatively accurate key-frame indexes, while ignoring the difficulty of obtaining accurate key-frame indexes and the objective existence of key-frame index errors, which impedes them from moving towards practical applications. In this paper, we propose CausalNet, a novel framework to achieve robust MER facing key-frame index errors while maintaining accurate recognition. To enhance robustness, CausalNet takes the representation of the entire ME sequence as the input. To address the information redundancy brought by the complete ME range input and maintain accurate recognition, first, the Causal Motion Position Learning Module (CMPLM) is proposed to help the model locate the muscle movement areas related to Action Units (AUs), thereby reducing the attention to other redundant areas. Second, the Causal Attention Block (CAB) is proposed to deeply learn the causal relationships between the muscle contraction and relaxation movements in MEs. Empirical experiments have demonstrated that on popular ME benchmarks, the CausalNet has achieved robust MER under different levels of key-frame index noise. Meanwhile, it has surpassed state-of-the-art (SOTA) methods on several standard MER benchmarks when using the provided annotated key-frames. Code is available at https://github.com/tony19980810/CausalNet.