Dual Stream Independence Decoupling for True Emotion Recognition under Masked Expressions
This addresses the challenge of emotion recognition in scenarios where people deliberately conceal their emotions, though it appears incremental as it builds on existing paradigms with a new frame selection and decoupling method.
The paper tackles the problem of recognizing true emotions from masked expressions by introducing an apexframe-based paradigm that uses frames with stable disguised states instead of onsetframes that leak true emotion information, and proposes a dual stream independence decoupling framework that improves recognition performance.
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.