Audio-Visual Compound Expression Recognition Method based on Late Modality Fusion and Rule-based Decision
This paper presents the results of the SUN team for the Compound Expressions Recognition Challenge of the 6th ABAW Competition. We propose a novel audio-visual method for compound expression recognition. Our method relies on emotion recognition models that fuse modalities at the emotion probability level, while decisions regarding the prediction of compound expressions are based on predefined rules. Notably, our method does not use any training data specific to the target task. Thus, the problem is a zero-shot classification task. The method is evaluated in multi-corpus training and cross-corpus validation setups. Using our proposed method is achieved an F1-score value equals to 22.01% on the C-EXPR-DB test subset. Our findings from the challenge demonstrate that the proposed method can potentially form a basis for developing intelligent tools for annotating audio-visual data in the context of human's basic and compound emotions.
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IEMOCAP
AffectNet
MELD
Aff-Wild
