  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. Our
ensemble model achieved F1 as 0.4133, accuracy as 0.6216 and final metric as
0.4821 on the validation set.
