Performance evaluation of fingerprint verification systems
Performance evaluation of fingerprint verification systems
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DOI：
10.1109/tpami.2006.20
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发表时间：
2006-01-01
影响因子：
23.6
通讯作者：
Jain, AK
中科院分区：
文献类型：
--
作者：
Cappelli, R;Maio, D;Jain, AK
本文主要研究指纹验证系统的性能评价问题。在对生物识别测试举措进行初步分类之后，我们通过介绍最近的指纹验证竞赛（FVC2004）的结果，探讨了与性能评估相关的理论和实践问题。FVC2004是由这项工作的作者组织的，目的是评估这一具有挑战性的模式识别应用的最新技术，并为基于指纹的生物识别系统的明确比较提供一个新的通用基准。FVC2004是在评估人员现场对评估人员的硬件进行的独立的、有严格监督的评估。这使得测试可以完全控制，并且可以公平地比较不同算法的计算时间。从以前类似的比赛（FVC2000和FVC2002）中获得的经验和反馈使我们能够改进FVC2004的组织和方法，并吸引更多的学术和商业组织的注意（FVC2004提交了67种算法）。加入了一个新的“轻”竞争类别，以估计由于施加计算限制而导致的匹配性能损失。本文讨论了数据收集和测试方案，并对结果进行了详细的分析。我们介绍了一种简单但有效的方法来比较得分水平上的算法，使我们能够隔离困难的情况（图像），并研究误差相关性和算法“融合”。所获得的大量信息，包括根据其特征对提交的算法进行结构化分类，使人们能够更好地了解当前指纹识别系统的工作原理，并为未来描绘有用的研究方向。
This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent Fingerprint Verification Competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators' site on evaluators' hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, "Light" competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm "fusion." The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future.