  The task of recognizing human facial expressions plays a vital role in
various human-related systems, including health care and medical fields. With
the recent success of deep learning and the accessibility of a large amount of
annotated data, facial expression recognition research has been mature enough
to be utilized in real-world scenarios with audio-visual datasets. In this
paper, we introduce Swin transformer-based facial expression approach for an
in-the-wild audio-visual dataset of the Aff-Wild2 Expression dataset.
Specifically, we employ a three-stream network (i.e., Visual stream, Temporal
stream, and Audio stream) for the audio-visual videos to fuse the multi-modal
information into facial expression recognition. Experimental results on the
Aff-Wild2 dataset show the effectiveness of our proposed multi-modal
approaches.
