# Facial Expression Recognition based on Multi-head Cross Attention Network

arXiv: https://arxiv.org/abs/2203.13235 · HTML: https://mlsanity.com/paper/2203.13235/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond](https://mlsanity.com/paper/1804.10938/) (2018)
- [Analysing Affective Behavior in the First ABAW 2020 Competition](https://mlsanity.com/paper/2001.11409/) (2020)
- [Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework](https://mlsanity.com/paper/2103.15792/) (2021)
- [Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study](https://mlsanity.com/paper/2105.03790/) (2021)
- [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)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [Learning Social Relation Traits from Face Images](https://mlsanity.com/paper/1509.03936/) (2015)

## Built on by (most cited first, 8 of 8)

- [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)
- [Affective Behaviour Analysis Using Pretrained Model with Facial Priori](https://mlsanity.com/paper/2207.11679/) (2022)
- [Exploring Expression-related Self-supervised Learning for Affective Behaviour Analysis](https://mlsanity.com/paper/2303.10511/) (2023)
- [Spatial-temporal Transformer for Affective Behavior Analysis](https://mlsanity.com/paper/2303.10561/) (2023)
- [A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition](https://mlsanity.com/paper/2603.12221/) (2026)

## Abstract (arXiv)

Facial expression in-the-wild is essential for various interactive computing domains. In this paper, we proposed an extended version of DAN model to address the VA estimation and facial expression challenges introduced in ABAW 2022. Our method produced preliminary results of 0.44 of mean CCC value for the VA estimation task, and 0.33 of the average F1 score for the expression classification task.