# Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network

arXiv: https://arxiv.org/abs/2002.00606 · HTML: https://mlsanity.com/paper/2002.00606/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport](https://mlsanity.com/paper/2401.15489/) (2024)
- [Multi Teacher Privileged Knowledge Distillation for Multimodal Expression Recognition](https://mlsanity.com/paper/2408.09035/) (2024)

## Abstract (arXiv)

This paper presents a neural network based method Multi-Task Affect Net(MTANet) submitted to the Affective Behavior Analysis in-the-Wild Challenge in FG2020. This method is a multi-task network and based on SE-ResNet modules. By utilizing multi-task learning, this network can estimate and recognize three quantified affective models: valence and arousal, action units, and seven basic emotions simultaneously. MTANet achieve Concordance Correlation Coefficient(CCC) rates of 0.28 and 0.34 for valence and arousal, F1-score of 0.427 and 0.32 for AUs detection and categorical emotion classification.