# Face Trees for Expression Recognition

arXiv: https://arxiv.org/abs/2112.02487 · HTML: https://mlsanity.com/paper/2112.02487/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [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)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [Reasoning about Entailment with Neural Attention](https://mlsanity.com/paper/1509.06664/) (2015)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)
- [Deep Learning using Linear Support Vector Machines](https://mlsanity.com/paper/1306.0239/) (2013)
- [Self-supervised Contrastive Learning of Multi-view Facial Expressions](https://mlsanity.com/paper/2108.06723/) (2021)
- [Facial Motion Prior Networks for Facial Expression Recognition](https://mlsanity.com/paper/1902.08788/) (2019)
- [Temporal Multimodal Fusion for Video Emotion Classification in the Wild](https://mlsanity.com/paper/1709.07200/) (2017)
- [BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient](https://mlsanity.com/paper/2004.08495/) (2020)
- [Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition](https://mlsanity.com/paper/2008.00859/) (2020)
- [Spatiotemporal Contrastive Learning of Facial Expressions in Videos](https://mlsanity.com/paper/2108.03064/) (2021)
- [CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routing](https://mlsanity.com/paper/2101.03503/) (2021)
- [CNN-based Facial Affect Analysis on Mobile Devices](https://mlsanity.com/paper/1807.08775/) (2018)
- [Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing](https://mlsanity.com/paper/1903.02110/) (2019)

## Built on by (most cited first, 6 of 6)

- [Analysis of Semi-Supervised Methods for Facial Expression Recognition](https://mlsanity.com/paper/2208.00544/) (2022)
- [FaceTopoNet: Facial Expression Recognition using Face Topology Learning](https://mlsanity.com/paper/2209.06322/) (2022)
- [Toward Fair Facial Expression Recognition with Improved Distribution Alignment](https://mlsanity.com/paper/2306.06696/) (2023)
- [Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition](https://mlsanity.com/paper/2311.06852/) (2023)
- [Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data](https://mlsanity.com/paper/2306.01229/) (2023)
- [Active Learning with Contrastive Pre-training for Facial Expression Recognition](https://mlsanity.com/paper/2307.02744/) (2023)

## Abstract (arXiv)

We propose an end-to-end architecture for facial expression recognition. Our model learns an optimal tree topology for facial landmarks, whose traversal generates a sequence from which we obtain an embedding to feed a sequential learner. The proposed architecture incorporates two main streams, one focusing on landmark positions to learn the structure of the face, while the other focuses on patches around the landmarks to learn texture information. Each stream is followed by an attention mechanism and the outputs are fed to a two-stream fusion component to perform the final classification. We conduct extensive experiments on two large-scale publicly available facial expression datasets, AffectNet and FER2013, to evaluate the efficacy of our approach. Our method outperforms other solutions in the area and sets new state-of-the-art expression recognition rates on these datasets.