指纹质量与人口统计学特征的大规模运营研究
A large-scale operational study of fingerprint quality and demographics
AI总结：
本文基于近1.6万名受试者的大规模运营数据，系统研究了性别、年龄和手指类型等人口统计学因素对指纹质量及匹配准确率的影响，揭示了不同人群间的性能差异，并提出解释假设与后续改进措施，以促进生物特征识别技术的算法公平性。
摘要：
尽管一些初步工作已在小规模数据集上显示出指纹识别技术性能在某些人口统计学群体上存在一定程度的偏差，但目前仍缺乏足够证据来理解性别、年龄或手指类型等因素对指纹质量以及进而对指纹匹配准确率可能产生的影响。本研究在一个包含近16,000名受试者十指印痕的大规模运营数据库上，探讨了这一仍未被充分研究的课题。所得结果进一步揭示了指纹质量与人口统计学特征之间的依赖关系，并表明基于指纹的识别系统确实在不同人群细分中存在一定程度的性能差异。在实验评估的基础上，本文指出了基于数据驱动证据的新观察结果，提出了可解释这些观察结果的合理假设，并以有助于减少所观察到的指纹质量差异的潜在后续措施作为结论。由此，本文可被视为进一步提高生物特征识别技术算法公平性与平等性的一项贡献。
英文摘要：
Even though a few initial works have shown on small sets of data some level of bias in the performance of fingerprint recognition technology with respect to certain demographic groups, there is still not sufficient evidence to understand the impact that certain factors such as gender, age or finger-type may have on fingerprint quality and, in turn, also on fingerprint matching accuracy. The present work addresses this still under researched topic, on a large-scale database of operational data containing 10-print impressions of almost 16,000 subjects. The results reached provide further insight into the dependency of fingerprint quality and demographics, and show that there in fact exists a certain degree of performance variability in fingerprint-based recognition systems for different segments of the population. Based on the experimental evaluation, the work points out new observations based on data-driven evidence, provides plausible hypotheses to explain such observations, and concludes with potential follow-up actions that can help to reduce the observed fingerprint quality differences. This way, the current paper can be considered as a contribution to further increase the algorithmic fairness and equality of biometric technology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
