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

TL;DR

This paper investigates how the racial composition of training data influences bias in face recognition systems, revealing that uniform racial distribution alone does not eliminate bias and highlighting the importance of face quality and data characteristics.

Contribution

It provides a comprehensive analysis of racial distribution effects, introduces the racial gradation metric, and evaluates the correlation between clustering and bias in face recognition models.

Findings

Uniform racial distribution does not guarantee bias-free recognition.

Face image quality significantly impacts model bias.

Clustering metrics have limited correlation with bias.

Abstract

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.