A Deep Dive into Dataset Imbalance and Bias in Face Identification

by   Valeriia Cherepanova, et al.

As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern. Media portrayals often center imbalance as the main source of bias, i.e., that FR models perform worse on images of non-white people or women because these demographic groups are underrepresented in training data. Recent academic research paints a more nuanced picture of this relationship. However, previous studies of data imbalance in FR have focused exclusively on the face verification setting, while the face identification setting has been largely ignored, despite being deployed in sensitive applications such as law enforcement. This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity. In this work, we address this gap in the research by thoroughly exploring the effects of each kind of imbalance possible in face identification, and discuss other factors which may impact bias in this setting.


page 2

page 7

page 12

page 14


Exploring Causes of Demographic Variations In Face Recognition Accuracy

In recent years, media reports have called out bias and racism in face r...

Demographic Fairness in Face Identification: The Watchlist Imbalance Effect

Recently, different researchers have found that the gallery composition ...

A Real Balanced Dataset For Understanding Bias? Factors That Impact Accuracy, Not Numbers of Identities and Images

The issue of disparities in face recognition accuracy across demographic...

On Demographic Bias in Fingerprint Recognition

Fingerprint recognition systems have been deployed globally in numerous ...

Asymmetric Rejection Loss for Fairer Face Recognition

Face recognition performance has seen a tremendous gain in recent years,...

Fairness Properties of Face Recognition and Obfuscation Systems

The proliferation of automated facial recognition in various commercial ...

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

Systems incorporating biometric technologies have become ubiquitous in p...

Please sign up or login with your details

Forgot password? Click here to reset