Spatial and Temporal Networks for Facial Expression Recognition in the Wild Videos

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The paper describes our proposed methodology for the seven basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2021.

• In this task, facial expression recognition (FER) methods aim to classify the correct expression category from a diverse background, but there are several challenges.
• First, to adapt the model to in-the-wild scenarios, we use the knowledge from pre-trained large-scale face recognition data.
• Second, we propose an ensemble model with a convolution neural network (CNN), a CNN-recurrent neural network (CNN-RNN), and a CNN-Transformer (CNN-Transformer), to incorporate both spatial and temporal information.