# Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition

arXiv: https://arxiv.org/abs/1802.06664 · HTML: https://mlsanity.com/paper/1802.06664/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [Meta Auxiliary Learning for Facial Action Unit Detection](https://mlsanity.com/paper/2105.06620/) (2021)
- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition](https://mlsanity.com/paper/2507.14608/) (2025)
- [Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images](https://mlsanity.com/paper/2104.11274/) (2021)
- [Facial Emotion Recognition: A multi-task approach using deep learning](https://mlsanity.com/paper/2110.15028/) (2021)

## Abstract (arXiv)

Automated emotion recognition in the wild from facial images remains a challenging problem. Although recent advances in Deep Learning have supposed a significant breakthrough in this topic, strong changes in pose, orientation and point of view severely harm current approaches. In addition, the acquisition of labeled datasets is costly, and current state-of-the-art deep learning algorithms cannot model all the aforementioned difficulties. In this paper, we propose to apply a multi-task learning loss function to share a common feature representation with other related tasks. Particularly we show that emotion recognition benefits from jointly learning a model with a detector of facial Action Units (collective muscle movements). The proposed loss function addresses the problem of learning multiple tasks with heterogeneously labeled data, improving previous multi-task approaches. We validate the proposal using two datasets acquired in non controlled environments, and an application to predict compound facial emotion expressions.