Fairness measures for biometric quality assessment

TL;DR

This paper proposes and compares multiple fairness measures for biometric quality assessment algorithms to ensure unbiased performance across demographic groups, aiming to support standardization in the field.

Contribution

It introduces novel fairness measures for biometric quality assessment, addressing demographic bias and guiding the development of fairer algorithms.

Findings

Proposed multiple fairness measures for biometric quality assessment.

Compared effectiveness of these measures across demographic groups.

Highlights potential for standardization in fairness evaluation.

Abstract

Quality assessment algorithms measure the quality of a captured biometric sample. Since the sample quality strongly affects the recognition performance of a biometric system, it is essential to only process samples of sufficient quality and discard samples of low-quality. Even though quality assessment algorithms are not intended to yield very different quality scores across demographic groups, quality score discrepancies are possible, resulting in different discard ratios. To ensure that quality assessment algorithms do not take demographic characteristics into account when assessing sample quality and consequently to ensure that the quality algorithms perform equally for all individuals, it is crucial to develop a fairness measure. In this work we propose and compare multiple fairness measures for evaluating quality components across demographic groups. Proposed measures, could be used as potential candidates for an upcoming standard in this important field.