Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information

Darshan Gera; S Balasubramanian

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arXiv · 2009.14440

Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information

Darshan Gera·S Balasubramanian

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Abstract

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.

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BibTeX RIS

Darshan Gera, S Balasubramanian. 2020-10-10. Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information. https://arxiv.org/abs/2009.14440

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