Audio-Visual Compound Expression Recognition Method based on Late Modality Fusion and Rule-based Decision
CoRR(2024)
Abstract
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. The method is evaluated in multi-corpus training
and cross-corpus validation setups. Our findings from the challenge demonstrate
that the proposed method can potentially form a basis for development of
intelligent tools for annotating audio-visual data in the context of human's
basic and compound emotions. The source code is publicly available.
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