# The Effect of Model Compression on Fairness in Facial Expression Recognition

arXiv: https://arxiv.org/abs/2201.01709 · HTML: https://mlsanity.com/paper/2201.01709/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Characterising Bias in Compressed Models](https://mlsanity.com/paper/2010.03058/) (2020)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [Loss Aware Post-training Quantization](https://mlsanity.com/paper/1911.07190/) (2019)
- [Investigating Bias and Fairness in Facial Expression Recognition](https://mlsanity.com/paper/2007.10075/) (2020)
- [On-Device Neural Net Inference with Mobile GPUs](https://mlsanity.com/paper/1907.01989/) (2019)
- [Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition](https://mlsanity.com/paper/2012.13912/) (2020)
- [Prune Responsibly](https://mlsanity.com/paper/2009.09936/) (2020)
- [Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning](https://mlsanity.com/paper/2011.05231/) (2020)
- [Training Behavior of Sparse Neural Network Topologies](https://mlsanity.com/paper/1810.00299/) (2018)

## Built on by (most cited first, 13 of 13)

- [Model Sparsity Can Simplify Machine Unlearning](https://mlsanity.com/paper/2304.04934/) (2023)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm](https://mlsanity.com/paper/2206.02976/) (2022)
- [Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition](https://mlsanity.com/paper/2308.11840/) (2023)
- [Model Compression Techniques in Biometrics Applications: A Survey](https://mlsanity.com/paper/2401.10139/) (2024)
- [What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias](https://mlsanity.com/paper/2410.08407/) (2024)
- [Toward Fair Facial Expression Recognition with Improved Distribution Alignment](https://mlsanity.com/paper/2306.06696/) (2023)
- [You Never Know: Quantization Induces Inconsistent Biases in Vision-Language Foundation Models](https://mlsanity.com/paper/2410.20265/) (2024)
- [Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race](https://mlsanity.com/paper/2502.20016/) (2025)
- [Is Adversarial Training with Compressed Datasets Effective?](https://mlsanity.com/paper/2402.05675/) (2024)
- [An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks](https://mlsanity.com/paper/2403.09441/) (2024)
- [Downsized and Compromised?: Assessing the Faithfulness of Model Compression](https://mlsanity.com/paper/2510.06125/) (2025)
- [Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models](https://mlsanity.com/paper/2603.05582/) (2026)

## Abstract (arXiv)

Deep neural networks have proved hugely successful, achieving human-like performance on a variety of tasks. However, they are also computationally expensive, which has motivated the development of model compression techniques which reduce the resource consumption associated with deep learning models. Nevertheless, recent studies have suggested that model compression can have an adverse effect on algorithmic fairness, amplifying existing biases in machine learning models. With this project we aim to extend those studies to the context of facial expression recognition. To do that, we set up a neural network classifier to perform facial expression recognition and implement several model compression techniques on top of it. We then run experiments on two facial expression datasets, namely the Extended Cohn-Kanade Dataset (CK+DB) and the Real-World Affective Faces Database (RAF-DB), to examine the individual and combined effect that compression techniques have on the model size, accuracy and fairness. Our experimental results show that: (i) Compression and quantisation achieve significant reduction in model size with minimal impact on overall accuracy for both CK+DB and RAF-DB; (ii) in terms of model accuracy, the classifier trained and tested on RAF-DB seems more robust to compression compared to the CK+ DB; (iii) for RAF-DB, the different compression strategies do not seem to increase the gap in predictive performance across the sensitive attributes of gender, race and age which is in contrast with the results on the CK+DB, where compression seems to amplify existing biases for gender. We analyse the results and discuss the potential reasons for our findings.