# The Impact of Racial Distribution in Training Data on Face Recognition Bias: A Closer Look

arXiv: https://arxiv.org/abs/2211.14498 · HTML: https://mlsanity.com/paper/2211.14498/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Deep Learning Face Representation by Joint Identification-Verification](https://mlsanity.com/paper/1406.4773/) (2014)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [Face Recognition: Too Bias, or Not Too Bias?](https://mlsanity.com/paper/2002.06483/) (2020)
- [Mitigating Face Recognition Bias via Group Adaptive Classifier](https://mlsanity.com/paper/2006.07576/) (2020)
- [SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance](https://mlsanity.com/paper/2103.05977/) (2021)
- [Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition](https://mlsanity.com/paper/2004.01019/) (2020)
- [Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems](https://mlsanity.com/paper/1712.01619/) (2017)
- [Deep Learning for Face Recognition: Pride or Prejudiced?](https://mlsanity.com/paper/1904.01219/) (2019)
- [Learning Fair Face Representation With Progressive Cross Transformer](https://mlsanity.com/paper/2108.04983/) (2021)

## Built on by (most cited first, 7 of 7)

- [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025)
- [Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics](https://mlsanity.com/paper/2212.02985/) (2022)
- [Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration](https://mlsanity.com/paper/2405.01273/) (2024)
- [What's color got to do with it? Face recognition in grayscale](https://mlsanity.com/paper/2309.05180/) (2023)
- [TrustSkin: A Fairness Pipeline for Trustworthy Facial Affect Analysis Across Skin Tone](https://mlsanity.com/paper/2505.20637/) (2025)
- [Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification](https://mlsanity.com/paper/2603.02475/) (2026)
- [Bias Mitigation in Face Recognition via Demographic-based Supervised Contrastive Learning](https://mlsanity.com/paper/2608.12971/) (2026)

## Abstract (arXiv)

Face recognition algorithms, when used in the real world, can be very useful, but they can also be dangerous when biased toward certain demographics. So, it is essential to understand how these algorithms are trained and what factors affect their accuracy and fairness to build better ones. In this study, we shed some light on the effect of racial distribution in the training data on the performance of face recognition models. We conduct 16 different experiments with varying racial distributions of faces in the training data. We analyze these trained models using accuracy metrics, clustering metrics, UMAP projections, face quality, and decision thresholds. We show that a uniform distribution of races in the training datasets alone does not guarantee bias-free face recognition algorithms and how factors like face image quality play a crucial role. We also study the correlation between the clustering metrics and bias to understand whether clustering is a good indicator of bias. Finally, we introduce a metric called racial gradation to study the inter and intra race correlation in facial features and how they affect the learning ability of the face recognition models. With this study, we try to bring more understanding to an essential element of face recognition training, the data. A better understanding of the impact of training data on the bias of face recognition algorithms will aid in creating better datasets and, in turn, better face recognition systems.