一个“差”的验证系统能成为一个“好的”识别系统吗?初步研究

Brian DeCann, A. Ross
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引用次数: 23

摘要

生物识别系统的匹配精度通常通过诸如错误匹配率(FMR)、错误不匹配率(FNMR)、等错误率(EER)、接收者工作特征(ROC)曲线和累积匹配特征(CMC)曲线等测量来量化。在这项工作中,我们分析了ROC曲线和CMC曲线之间的关系,这两个曲线分别是常用来描述验证和识别系统性能的两个指标。我们通过演示产生此类结果所需的条件,确定生物识别系统有可能表现出“良好”的验证性能和“差”的识别性能(反之亦然)。使用合成匹配分数的实验分析证实了我们的假设,即ROC或CMC本身不能完全表征生物识别系统的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can a “poor” verification system be a “good” identification system? A preliminary study
The matching accuracy of a biometric system is typically quantified through measures such as the False Match Rate (FMR), False Non-match Rate (FNMR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) curve and Cumulative Match Characteristic (CMC) curve. In this work, we analyze the relationship between the ROC and CMC curves, which are two measures commonly used to describe the performance of verification and identification systems, respectively. We establish that it is possible for a biometric system to exhibit “good” verification performance and “poor” identification performance (and vice versa) by demonstrating the conditions required to produce such outcomes. Experimental analysis using synthetically generated match scores confirms our hypothesis that the ROC or CMC alone cannot completely characterize biometric system performance.
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