# Facial Expression Recognition Using Disentangled Adversarial Learning

arXiv: https://arxiv.org/abs/1909.13135 · HTML: https://mlsanity.com/paper/1909.13135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network](https://mlsanity.com/paper/2201.09061/) (2022)
- [FExGAN-Meta: Facial Expression Generation with Meta Humans](https://mlsanity.com/paper/2203.05975/) (2022)
- [Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal](https://mlsanity.com/paper/2106.01467/) (2021)

## Abstract (arXiv)

The representation used for Facial Expression Recognition (FER) usually contain expression information along with other variations such as identity and illumination. In this paper, we propose a novel Disentangled Expression learning-Generative Adversarial Network (DE-GAN) to explicitly disentangle facial expression representation from identity information. In this learning by reconstruction method, facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN. The process of expression image reconstruction and disentangled expression representation learning is improved by performing expression and identity classification in the discriminator of DE-GAN. The disentangled facial expression representation is then used for facial expression recognition employing simple classifiers like SVM or MLP. The experiments are performed on publicly available and widely used face expression databases (CK+, MMI, Oulu-CASIA). The experimental results show that the proposed technique produces comparable results with state-of-the-art methods.