mlsanityData to 14 Oct 2026

Face Trees for Expression Recognition

arXiv 2112.02487 ↗First posted 5 Dec 2021cs.CV cs.LGcited by 6

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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.

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

20 of 42 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  3. 2015Gated Graph Sequence Neural Networkscited by 796
  4. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
  5. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 302
  6. 2018Deep Facial Expression Recognition: A Surveycited by 270
  7. 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognitioncited by 157
  8. 2019Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognitioncited by 127
  9. 2015Reasoning about Entailment with Neural Attentioncited by 69
  10. 2015Going Deeper in Facial Expression Recognition using Deep Neural Networkscited by 43
  11. 2013Deep Learning using Linear Support Vector Machinescited by 39
  12. 2021Self-supervised Contrastive Learning of Multi-view Facial Expressionscited by 23
8 more
  1. 2019Facial Motion Prior Networks for Facial Expression Recognitioncited by 20
  2. 2017Temporal Multimodal Fusion for Video Emotion Classification in the Wildcited by 18
  3. 2020BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradientcited by 13
  4. 2020Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognitioncited by 12
  5. 2021Spatiotemporal Contrastive Learning of Facial Expressions in Videoscited by 11
  6. 2021CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routingcited by 9
  7. 2018CNN-based Facial Affect Analysis on Mobile Devicescited by 5
  8. 2019Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computingcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 4.

6 papers.

  1. 2022Analysis of Semi-Supervised Methods for Facial Expression Recognitioncited by 4
  2. 2022FaceTopoNet: Facial Expression Recognition using Face Topology Learningcited by 4
  3. 2023Toward Fair Facial Expression Recognition with Improved Distribution Alignmentcited by 2
  4. 2023Contrastive Learning of View-Invariant Representations for Facial Expressions Recognitioncited by 2
  5. 2023Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Datacited by 1
  6. 2023Active Learning with Contrastive Pre-training for Facial Expression Recognitioncited by 0