# Recognizing Facial Expressions in the Wild using Multi-Architectural Representations based Ensemble Learning with Distillation

arXiv: https://arxiv.org/abs/2106.16126 · HTML: https://mlsanity.com/paper/2106.16126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [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)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Deep Learning using Linear Support Vector Machines](https://mlsanity.com/paper/1306.0239/) (2013)
- [Facial Expression Recognition using Convolutional Neural Networks: State of the Art](https://mlsanity.com/paper/1612.02903/) (2016)
- [Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2106.03487/) (2021)
- [Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation](https://mlsanity.com/paper/2106.04332/) (2021)

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

- [Interruptions detection in video conferences](https://mlsanity.com/paper/2303.02052/) (2023)

## Abstract (arXiv)

Facial expressions are the most common universal forms of body language. In the past few years, automatic facial expression recognition (FER) has been an active field of research. However, it is still a challenging task due to different uncertainties and complications. Nevertheless, efficiency and performance are yet essential aspects for building robust systems. We proposed two models, EmoXNet which is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time. Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07% test accuracy on FER2013 with FER+ annotations and 86.25% test accuracy on RAF-DB. Moreover, the distilled model (EmoXNetLite) showed 82.07% test accuracy on FER2013 with FER+ annotations and 81.78% test accuracy on RAF-DB. Results show that our models seem to generalize well on new data and are learned to focus on relevant facial representations for expressions recognition.