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Facial expression recognition is an essential task for various applications,including emotion detection, mental health analysis, and human-machineinteractions. In this paper, we propose a multi-modal facial expressionrecognition method that exploits audio information along with facial images toprovide a crucial clue to differentiate some ambiguous facial expressions.Specifically, we introduce a Modal Fusion Module (MFM) to fuse audio-visualinformation, where image and audio features are extracted from SwinTransformer. Additionally, we tackle the imbalance problem in the dataset byemploying dynamic data resampling. Our model has been evaluated in theAffective Behavior in-the-wild (ABAW) challenge of CVPR 2023.

Multi Modal Facial Expression Recognition with Transformer-Based Fusion Networks and Dynamic Sampling