论文标题
Ice-Gan：具有基于图的理由，用于微表达和合成
ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis
论文作者
论文摘要
微表达是对人们的真实感受和动机的反思，它们吸引了越来越多的研究人员进入自动面部微型表达识别的研究。简短的检测窗口，微妙的面部肌肉运动以及有限的训练样本使微观表达识别具有挑战性。为此，我们提出了一种具有基于图的推理（ICE-GAN）的新型身份感知和胶囊增强的生成对抗网络，将微表达合成作为辅助任务，以帮助识别。发电机产生具有可控的微表达和身份感知特征的合成面，其长期依赖性是通过图形推理模块（GRM）捕获的，并且歧视器检测图像真实性和表达式类。对2019年微型表达大挑战赛（MEGC2019）进行了评估，比获胜者有了显着提高（12.9％），并超过了其他最先进的方法。
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.
