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Score Fusion in Multibiometric Identification Based on Fuzzy Set Theory

机译:基于模糊集理论的多学识别分数融合

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Multimodal biometric systems consolidate or fuse information from multiple biometric sources. They have been developed to overcome several limitations of each individual biometric system, such as sensitivity to noise, intra class invariability, data quality, non-universality and other factors. In this paper, we propose a general framework of multibiometric identification system based on fusion at matching score level using fuzzy set theory. The motivation for using fuzzy set theory is that it offers methods suited to treat (modeling, fusion,...) and take into account the information inherently uncertain and ambiguous. We note that our fusion system is based on face and iris modalities. Experimental results exhibit that the proposed method performance bring obvious improvement compared to unimodal biometric identification methods and classical combination approaches at score level fusion.
机译:多模式生物识别系统整合或熔断来自多个生物识别源的信息。他们已经开发出来克服每个生物识别系统的几个限制,例如对噪声的敏感性,课外不变性,数据质量,非普遍性和其他因素。本文采用模糊集理论提出了基于匹配得分水平的融合的多学识别系统的一般框架。使用模糊集理论的动机是它提供适合治疗的方法(建模,融合,......),并考虑到本质上不确定和暧昧的信息。我们注意到我们的融合系统基于面部和虹膜模式。实验结果表明,与单峰的生物识别方法和刻度水平融合中的古典联合方法相比,所提出的方法性能具有明显的改进。

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