# Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal

arXiv: https://arxiv.org/abs/2106.01467 · HTML: https://mlsanity.com/paper/2106.01467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 81 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 30 of 33)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Domain Generalization for Object Recognition with Multi-task Autoencoders](https://mlsanity.com/paper/1508.07680/) (2015)
- [Episodic Training for Domain Generalization](https://mlsanity.com/paper/1902.00113/) (2019)
- [Simultaneous Deep Transfer Across Domains and Tasks](https://mlsanity.com/paper/1510.02192/) (2015)
- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications](https://mlsanity.com/paper/1606.03237/) (2016)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [Exploring Disentangled Feature Representation Beyond Face Identification](https://mlsanity.com/paper/1804.03487/) (2018)
- [FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition](https://mlsanity.com/paper/1609.06591/) (2016)
- [Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks](https://mlsanity.com/paper/2001.06338/) (2020)
- [Peak-Piloted Deep Network for Facial Expression Recognition](https://mlsanity.com/paper/1607.06997/) (2016)
- [Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?](https://mlsanity.com/paper/1510.02969/) (2015)
- [Zero-bias autoencoders and the benefits of co-adapting features](https://mlsanity.com/paper/1402.3337/) (2014)
- [Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network](https://mlsanity.com/paper/1903.08051/) (2019)
- [Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)](https://mlsanity.com/paper/2009.05938/) (2020)
- [Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines](https://mlsanity.com/paper/1604.03225/) (2016)
- [An Analysis of Unsupervised Pre-training in Light of Recent Advances](https://mlsanity.com/paper/1412.6597/) (2014)
- [Deep generative-contrastive networks for facial expression recognition](https://mlsanity.com/paper/1703.07140/) (2017)
- [Facial expression recognition based on local region specific features and support vector machines](https://mlsanity.com/paper/1604.04337/) (2016)

## Abstract (arXiv)

Bringing empathy to a computerized system could significantly improve the quality of human-computer communications, as soon as machines would be able to understand customer intentions and better serve their needs. According to different studies (Literature Review), visual information is one of the most important channels of human interaction and contains significant behavioral signals, that may be captured from facial expressions. Therefore, it is consistent and natural that the research in the field of Facial Expression Recognition (FER) has acquired increased interest over the past decade due to having diverse application area including health-care, sociology, psychology, driver-safety, virtual reality, cognitive sciences, security, entertainment, marketing, etc. We propose a new architecture for the task of FER and examine the impact of domain discrimination loss regularization on the learning process. With regard to observations, including both classical training conditions and unsupervised domain adaptation scenarios, important aspects of the considered domain adaptation approach integration are traced. The results may serve as a foundation for further research in the field.