# FERAtt: Facial Expression Recognition with Attention Net

arXiv: https://arxiv.org/abs/1902.03284 · HTML: https://mlsanity.com/paper/1902.03284/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 9 of 9)

- [Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition](https://mlsanity.com/paper/2109.07270/) (2021)
- [Expression Snippet Transformer for Robust Video-based Facial Expression Recognition](https://mlsanity.com/paper/2109.08409/) (2021)
- [ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning](https://mlsanity.com/paper/2305.01486/) (2023)
- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis](https://mlsanity.com/paper/2203.12570/) (2022)
- [EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge](https://mlsanity.com/paper/2306.08657/) (2023)
- [What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors](https://mlsanity.com/paper/2109.05457/) (2021)
- [SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2303.07648/) (2023)
- [GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution](https://mlsanity.com/paper/2410.15927/) (2024)

## Abstract (arXiv)

We present a new end-to-end network architecture for facial expression recognition with an attention model. It focuses attention in the human face and uses a Gaussian space representation for expression recognition. We devise this architecture based on two fundamental complementary components: (1) facial image correction and attention and (2) facial expression representation and classification. The first component uses an encoder-decoder style network and a convolutional feature extractor that are pixel-wise multiplied to obtain a feature attention map. The second component is responsible for obtaining an embedded representation and classification of the facial expression. We propose a loss function that creates a Gaussian structure on the representation space. To demonstrate the proposed method, we create two larger and more comprehensive synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We compared results with the PreActResNet18 baseline. Our experiments on these datasets have shown the superiority of our approach in recognizing facial expressions.