On Demographic Bias in Fingerprint Recognition

TL;DR

This paper introduces a statistical framework to detect demographic biases in fingerprint recognition systems, analyzing performance across different groups and showing bias reduction with improved accuracy.

Contribution

It presents a formal method to test for demographic bias in fingerprint matchers and evaluates bias across major demographic groups using real datasets.

Findings

Bias decreases as matcher accuracy improves

Small biases are often due to low-quality fingerprint images

State-of-the-art systems show minimal demographic differentials

Abstract

Fingerprint recognition systems have been deployed globally in numerous applications including personal devices, forensics, law enforcement, banking, and national identity systems. For these systems to be socially acceptable and trustworthy, it is critical that they perform equally well across different demographic groups. In this work, we propose a formal statistical framework to test for the existence of bias (demographic differentials) in fingerprint recognition across four major demographic groups (white male, white female, black male, and black female) for two state-of-the-art (SOTA) fingerprint matchers operating in verification and identification modes. Experiments on two different fingerprint databases (with 15,468 and 1,014 subjects) show that demographic differentials in SOTA fingerprint recognition systems decrease as the matcher accuracy increases and any small bias that may be evident is likely due to certain outlier, low-quality fingerprint images.