Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information
This work addresses the problem of unreliable expression recognition in real-world conditions for human-computer interaction, but it is incremental as it builds on existing attention mechanisms.
The authors tackled facial expression recognition in unconstrained environments by proposing a framework combining spatial-channel attention and complementary context information, achieving competitive performance on the Aff-Wild2 dataset.
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.