单虹膜双探针识别系统中的分数融合策略

P. Drozdowski, N. Wiegand, C. Rathgeb, C. Busch
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引用次数: 1

摘要

在生物识别系统的比较阶段,可以利用多个样本,通过信息融合或决策启发式来提高其生物识别性能。研究表明,在单实例双探针设置中,融合探针分数比单探针基线产生显著的生物识别性能提高。此外,Cheng等人的研究表明,使用探针-探针比较评分可以进一步提高指纹识别系统的生物识别性能。本文通过对CASIA-IrisV4-Interval数据集和BioSecure数据集的虹膜语料库进行基准测试,证明了上述方法对于虹膜识别系统是可行的。然而,由于它需要一个额外的参数,必须根据经验估计,我们提出了一种更简单的方法,它具有类似的生物识别性能,同时不需要额外的参数化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Score Fusion Strategies in Single-Iris Dual-Probe Recognition Systems
Multiple samples can be utilised at the comparison stage of a biometric system in order to increase its biometric performance via information fusion or decision heuristics. It has been shown, that in a single-instance dual-probe setup, fusing the probe scores yields significant biometric performance increase over the single-probe baseline. Additionally, using the probe-probe comparison score was demonstrated to further improve the biometric performance of a fingerprint recognition system in a study by Cheng et al. In this paper, through a benchmark on the CASIA-IrisV4-Interval dataset and on the iris corpus of the BioSecure dataset, the aforementioned method is shown to be viable for an iris recognition system. However, since it requires an additional parameter, which must be estimated empirically, we propose a simpler method which exhibits similar biometric performance, while requiring no additional parametrisation.
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