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

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.

arXiv commentarXiv admin note: text overlap with arXiv:2007.10298 (ABAW2020 challenge test set results added)

References in corpus (6)

• AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
• Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
• Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition
• Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition
• Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace
• Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition

Cited by in corpus (3)

• Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition
• Multi-modal Affect Analysis using standardized data within subjects in the Wild
• Machine vision detection to daily facial fatigue with a nonlocal 3D attention network