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Score Fusion in Multibiometric Identification Based on Fuzzy Set Theory

机译:基于模糊集理论的生物识别中的分数融合

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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合并或融合来自多个生物识别源的信息。开发它们是为了克服每个单独的生物识别系统的一些限制,例如对噪声的敏感性,组内不变性,数据质量,非通用性和其他因素。在本文中,我们提出了一种基于模糊集理论的,在匹配得分水平上融合的多生物识别系统的通用框架。使用模糊集理论的动机是,它提供适合处理(建模,融合等)的方法,并考虑到固有地不确定和模棱两可的信息。我们注意到,我们的融合系统基于面部和虹膜模态。实验结果表明,与单峰生物特征识别方法和经典组合方法相比,所提出的方法性能在分数水平融合上具有明显的改进。

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