  Facial expression recognition(FER) in the wild is crucial for building
reliable human-computer interactive systems. However, current FER systems fail
to perform well under various natural and un-controlled conditions. This report
presents attention based framework used in our submission to expression
recognition track of the Affective Behaviour Analysis in-the-wild (ABAW) 2020
competition. Spatial-channel attention net(SCAN) is used to extract local and
global attentive features without seeking any information from landmark
detectors. SCAN is complemented by a complementary context information(CCI)
branch which uses efficient channel attention(ECA) to enhance the relevance of
features. The performance of the model is validated on challenging Aff-Wild2
dataset for categorical expression classification.
