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.
@article{arxiv.2009.14440,
title = {Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information},
author = {Darshan Gera and S Balasubramanian},
journal= {arXiv preprint arXiv:2009.14440},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:2007.10298 (ABAW2020 challenge test set results added)