从遮蔽表情的巅峰帧识别真实情绪，避免伪装干扰。
Dual Stream Independence Decoupling for True Emotion Recognition under Masked Expressions
- 以稳定伪装状态的巅峰帧为输入，替代易泄露信息的起始帧。
- 提出双流解耦框架，使真实与伪装情绪特征相互独立。
- 在真实情绪识别上显著提升性能，适合面部遮挡场景应用。
从被遮蔽的表情中识别真实情绪极具挑战性，因人为刻意掩饰所致。现有方法基于仅开始伪装的起始帧（onset frame）进行识别，但该阶段仍泄露真实情绪信息，未能反映稳定的伪装状态。为此，本文提出一种基于巅峰帧（apex frame）的新范式，即从已达到稳定伪装状态的帧中识别真实情绪。进一步，设计了双流独立解耦框架，将真实情绪与伪装表达特征解耦，避免伪装情绪对真实情绪判断的干扰。为实现高效解耦，构建包含两个分类损失（分别学习真实情绪与伪装特征）和一个希尔伯特-施密特独立性损失（HSIC）的损失组，增强两特征间的独立性。实验表明，该巅峰帧范式更具挑战性，但所提解耦框架显著提升了识别性能。
原文摘要 · Abstract (English)
Recongnizing true emotions from masked expressions is extremely challenging due to deliberate concealment. Existing paradigms recognize true emotions from masked-expression clips that contain onsetframes just starting to disguise. However, this paradigm may not reflect the actual disguised state, as the onsetframe leaks the true emotional information without reaching a stable disguise state. Thus, this paper introduces a novel apexframe-based paradigm that classifies true emotions from the apexframe with a stable disguised state. Furthermore, this paper proposes a novel dual stream independence decoupling framework that decouples true and disguised emotion features, avoiding the interference of disguised emotions on true emotions. For efficient decoupling, we design a decoupling loss group, comprising two classification losses that learn true emotion and disguised expression features, respectively, and a Hilbert-Schmidt Independence loss that enhances the independence of two features. Experiments demonstrate that the apexframe-based paradigm is challenging, and the proposed decouple framework improves recogntion performances.
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