ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis

TL;DR

This paper introduces ICE-GAN, a novel generative adversarial network that enhances micro-expression recognition by synthesizing micro-expressions with identity-aware features and graph-based reasoning, significantly improving recognition accuracy.

Contribution

The paper proposes ICE-GAN, integrating micro-expression synthesis, identity-awareness, and graph reasoning to improve recognition performance beyond existing methods.

Findings

Achieved 12.9% higher accuracy on MEGC2019 dataset.

Outperformed state-of-the-art micro-expression recognition methods.

Effectively synthesizes controllable micro-expressions for training enhancement.

Abstract

Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition. The short detection window, the subtle facial muscle movements, and the limited training samples make micro-expression recognition challenging. To this end, we propose a novel Identity-aware and Capsule-Enhanced Generative Adversarial Network with graph-based reasoning (ICE-GAN), introducing micro-expression synthesis as an auxiliary task to assist recognition. The generator produces synthetic faces with controllable micro-expressions and identity-aware features, whose long-ranged dependencies are captured through the graph reasoning module (GRM), and the discriminator detects the image authenticity and expression classes. Our ICE-GAN was evaluated on Micro-Expression Grand Challenge 2019 (MEGC2019) with a significant improvement (12.9%) over the winner and surpassed other state-of-the-art methods.