# A Multi-resolution Approach to Expression Recognition in the Wild

arXiv: https://arxiv.org/abs/2103.05723 · HTML: https://mlsanity.com/paper/2103.05723/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [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)
- [Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition](https://mlsanity.com/paper/1811.07770/) (2018)
- [Deep Learning using Linear Support Vector Machines](https://mlsanity.com/paper/1306.0239/) (2013)
- [A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions](https://mlsanity.com/paper/1811.07771/) (2018)
- [Deep Learning for Human Affect Recognition: Insights and New Developments](https://mlsanity.com/paper/1901.02884/) (2019)
- [Cross-Resolution Learning for Face Recognition](https://mlsanity.com/paper/1912.02851/) (2019)
- [Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks](https://mlsanity.com/paper/1705.07871/) (2017)
- [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)

## Abstract (arXiv)

Facial expressions play a fundamental role in human communication. Indeed, they typically reveal the real emotional status of people beyond the spoken language. Moreover, the comprehension of human affect based on visual patterns is a key ingredient for any human-machine interaction system and, for such reasons, the task of Facial Expression Recognition (FER) draws both scientific and industrial interest. In the recent years, Deep Learning techniques reached very high performance on FER by exploiting different architectures and learning paradigms. In such a context, we propose a multi-resolution approach to solve the FER task. We ground our intuition on the observation that often faces images are acquired at different resolutions. Thus, directly considering such property while training a model can help achieve higher performance on recognizing facial expressions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset. Not being available a test set, we conduct tests and models selection by employing the validation set only on which we achieve more than 90% accuracy on classifying the seven expressions that the dataset comprises.