# Action Units Recognition Using Improved Pairwise Deep Architecture

arXiv: https://arxiv.org/abs/2107.03143 · HTML: https://mlsanity.com/paper/2107.03143/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [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)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition](https://mlsanity.com/paper/1811.07770/) (2018)
- [Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing](https://mlsanity.com/paper/1704.03067/) (2017)
- [A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions](https://mlsanity.com/paper/1811.07771/) (2018)
- [Action Units Recognition by Pairwise Deep Architecture](https://mlsanity.com/paper/2010.00288/) (2020)

## Built on by (most cited first, 6 of 6)

- [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)
- [An Attention-based Method for Action Unit Detection at the 3rd ABAW Competition](https://mlsanity.com/paper/2203.12428/) (2022)

## Abstract (arXiv)

Facial Action Units (AUs) represent a set of facial muscular activities and various combinations of AUs can represent a wide range of emotions. AU recognition is often used in many applications, including marketing, healthcare, education, and so forth. Although a lot of studies have developed various methods to improve recognition accuracy, it still remains a major challenge for AU recognition. In the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition, we proposed a new automatic Action Units (AUs) recognition method using a pairwise deep architecture to derive the Pseudo-Intensities of each AU and then convert them into predicted intensities. This year, we introduced a new technique to last year's framework to further reduce AU recognition errors due to temporary face occlusion such as hands on face or large face orientation. We obtained a score of 0.65 in the validation data set for this year's competition.