# Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition

arXiv: https://arxiv.org/abs/2311.06852 · HTML: https://mlsanity.com/paper/2311.06852/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 79 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 30 of 33)

- [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)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [Analyzing and Improving Representations with the Soft Nearest Neighbor Loss](https://mlsanity.com/paper/1902.01889/) (2019)
- [Weakly Supervised Contrastive Learning](https://mlsanity.com/paper/2110.04770/) (2021)
- [Improving Generalization via Scalable Neighborhood Component Analysis](https://mlsanity.com/paper/1808.04699/) (2018)
- [Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin](https://mlsanity.com/paper/2203.12341/) (2022)
- [Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2103.09154/) (2021)
- [Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks](https://mlsanity.com/paper/2001.06338/) (2020)
- [Self-supervised Contrastive Learning of Multi-view Facial Expressions](https://mlsanity.com/paper/2108.06723/) (2021)
- [A Multi-task Mean Teacher for Semi-supervised Facial Affective Behavior Analysis](https://mlsanity.com/paper/2107.04225/) (2021)
- [BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient](https://mlsanity.com/paper/2004.08495/) (2020)
- [Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging](https://mlsanity.com/paper/1901.00449/) (2018)
- [Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher](https://mlsanity.com/paper/2205.14361/) (2022)
- [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)
- [Face Trees for Expression Recognition](https://mlsanity.com/paper/2112.02487/) (2021)

## Built on by (most cited first, 2 of 2)

- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [FIDA: Feature Instability-Driven Attack on Self-Supervised Facial Representation](https://mlsanity.com/paper/2608.26861/) (2026)

## Abstract (arXiv)

Although there has been much progress in the area of facial expression recognition (FER), most existing methods suffer when presented with images that have been captured from viewing angles that are non-frontal and substantially different from those used in the training process. In this paper, we propose ViewFX, a novel view-invariant FER framework based on contrastive learning, capable of accurately classifying facial expressions regardless of the input viewing angles during inference. ViewFX learns view-invariant features of expression using a proposed self-supervised contrastive loss which brings together different views of the same subject with a particular expression in the embedding space. We also introduce a supervised contrastive loss to push the learnt view-invariant features of each expression away from other expressions. Since facial expressions are often distinguished with very subtle differences in the learned feature space, we incorporate the Barlow twins loss to reduce the redundancy and correlations of the representations in the learned representations. The proposed method is a substantial extension of our previously proposed CL-MEx, which only had a self-supervised loss. We test the proposed framework on two public multi-view facial expression recognition datasets, KDEF and DDCF. The experiments demonstrate that our approach outperforms previous works in the area and sets a new state-of-the-art for both datasets while showing considerably less sensitivity to challenging angles and the number of output labels used for training. We also perform detailed sensitivity and ablation experiments to evaluate the impact of different components of our model as well as its sensitivity to different parameters.