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

arXiv: https://arxiv.org/abs/2009.14440 · HTML: https://mlsanity.com/paper/2009.14440/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges](https://mlsanity.com/paper/2202.10659/) (2022)
- [ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.01138/) (2022)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition](https://mlsanity.com/paper/2402.19344/) (2024)
- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition](https://mlsanity.com/paper/2107.03708/) (2021)
- [Multitask Multi-database Emotion Recognition](https://mlsanity.com/paper/2107.04127/) (2021)
- [Facial Expression Classification using Fusion of Deep Neural Network in Video for the 3rd ABAW3 Competition](https://mlsanity.com/paper/2203.12899/) (2022)
- [Multi-modal Affect Analysis using standardized data within subjects in the Wild](https://mlsanity.com/paper/2107.03009/) (2021)
- [A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2201.06868/) (2022)
- [ARPGNet: Appearance- and Relation-aware Parallel Graph Attention Fusion Network for Facial Expression Recognition](https://mlsanity.com/paper/2511.22188/) (2025)
- [Machine vision detection to daily facial fatigue with a nonlocal 3D attention network](https://mlsanity.com/paper/2104.10420/) (2021)

## Abstract (arXiv)

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.