  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.
