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.

本研究解决了生物识别质量评估算法在不同人口群体中可能出现的质量评分差异问题。提出了多种公平性指标，用于评估不同群体之间的质量成分，旨在确保算法在样本质量评估时不受人口特征的影响。研究结果为制定该领域的潜在标准提供了重要依据。 

生物识别质量评估的公平性指标