# Facial Emotion Recognition: A multi-task approach using deep learning

arXiv: https://arxiv.org/abs/2110.15028 · HTML: https://mlsanity.com/paper/2110.15028/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition](https://mlsanity.com/paper/1606.02894/) (2016)
- [Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines](https://mlsanity.com/paper/1604.03225/) (2016)
- [A Real Time Facial Expression Classification System Using Local Binary Patterns](https://mlsanity.com/paper/1505.04058/) (2015)
- [Facial Emotion Recognition using Convolutional Neural Networks](https://mlsanity.com/paper/1910.05602/) (2019)
- [Facial expression recognition based on local region specific features and support vector machines](https://mlsanity.com/paper/1604.04337/) (2016)
- [Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition](https://mlsanity.com/paper/1802.06664/) (2018)

## Built on by (most cited first, 1 of 1)

- [NAC-TCN: Temporal Convolutional Networks with Causal Dilated Neighborhood Attention for Emotion Understanding](https://mlsanity.com/paper/2312.07507/) (2023)

## Abstract (arXiv)

Facial Emotion Recognition is an inherently difficult problem, due to vast differences in facial structures of individuals and ambiguity in the emotion displayed by a person. Recently, a lot of work is being done in the field of Facial Emotion Recognition, and the performance of the CNNs for this task has been inferior compared to the results achieved by CNNs in other fields like Object detection, Facial recognition etc. In this paper, we propose a multi-task learning algorithm, in which a single CNN detects gender, age and race of the subject along with their emotion. We validate this proposed methodology using two datasets containing real-world images. The results show that this approach is significantly better than the current State of the art algorithms for this task.