Personal Identification Using Multibiometrics Rank-Level Fusion
Personal Identification Using Multibiometrics Rank-Level Fusion
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DOI：
10.1109/tsmcc.2010.2089516
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发表时间：
2011-09-01
期刊：
影响因子：
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通讯作者：
Shekhar, Sumit
中科院分区：
文献类型：
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作者：
Kumar, Ajay;Shekhar, Sumit
本文研究了一种使用多种生物特征表示的等级组合进行个人识别的新方法。人们很少致力于研究多生物特征组合的等级级融合方法，也没有使用多个掌纹表示。在本文中，我们提出了一种新的非线性等级级融合方法，并对等级级融合方法进行了比较研究，这对于结合多生物特征融合非常有用。给出了根据公开的多生物特征得分和真实手部生物特征数据，使用 Borda 计数、逻辑回归/加权 Borda 计数、最高等级方法和 Bucklin 方法评估/确定等级级别组合的比较实验结果。我们在本文中提出的实验结果表明，与单个掌纹表示相比，可以实现识别准确性的显着性能提高。本文提出的严格实验结果还表明，所提出的非线性排序级别方法优于本文提出的排序级别组合方法。
This paper investigates a new approach for the personal recognition using rank-level combination of multiple biometrics representations. There has been very little effort to study rank-level fusion approaches for multibiometrics combination and none using multiple palmprint representations. In this paper, we propose a new nonlinear rank-level fusion approach and present a comparative study of rank-level fusion approaches, which can be useful in combining multibiometrics fusion. The comparative experimental results from the publicly available multibiometrics scores and real hand biometrics data to evaluate/ascertain the rank-level combination using Borda count, logistic regression/weighted Borda count, highest rank method, and Bucklin method are presented. Our experimental results presented in this paper suggest that significant performance improvement in the recognition accuracy can be achieved as compared to those from individual palmprint representations. The rigorous experimental results presented in this paper also suggest that the proposed nonlinear rank-level approach outperforms the rank-level combination approaches presented in this paper.