Score normalization in multimodal biometric systems
Score normalization in multimodal biometric systems
复制标题
DOI：
10.1016/j.patcog.2005.01.012
复制
发表时间：
2005-12-01
影响因子：
8
通讯作者：
Ross, A
中科院分区：
文献类型：
--
作者：
Jain, A;Nandakumar, K;Ross, A
多模态生物特征系统整合了由多个生物特征源提供的证据，并且与基于单个生物特征模态的系统相比通常提供更好的识别性能。虽然多模态系统中的信息融合可以在各个级别上执行，但由于容易访问和组合由不同匹配器生成的分数，因此在匹配分数级别上的集成是最常见的方法。由于由各种模态输出的匹配分数是异构的，因此在将它们组合之前，需要将这些分数归一化以将这些分数转换到公共域中。在本文中，我们已经研究了不同的归一化技术和融合规则的性能的背景下，多模态生物识别系统的基础上，面对，指纹和手的几何特征的用户。在100个用户的数据库上进行的实验表明，最小最大值，z分数，双曲正切归一化方案的应用，其次是一个简单的分数融合方法的结果在更好的识别性能相比，其他方法。然而，实验还表明，最小值-最大值和z分数归一化技术对数据中的离群值敏感，突出了对像tanh归一化这样的鲁棒且有效的归一化过程的需求。还观察到，与为所有用户的多个生物特征分配相同权重集的系统相比，利用用户特定权重的多模式系统表现更好。(c)2005模式识别学会。由爱思唯尔有限公司出版。保留所有权利。
Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. Although information fusion in a multimodal system can be performed at various levels, integration at the matching score level is the most common approach due to the ease in accessing and combining the scores generated by different matchers. Since the matching scores output by the various modalities are heterogeneous, score normalization is needed to transform these scores into a common domain, prior to combining them. In this paper, we have studied the performance of different normalization techniques and fusion rules in the context of a multimodal biometric system based on the face, fingerprint and hand-geometry traits of a user. Experiments conducted on a database of 100 users indicate that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods. However, experiments also reveal that the min-max and z-score normalization techniques are sensitive to outliers in the data, highlighting the need for a robust and efficient normalization procedure like the tanh normalization. It was also observed that multimodal systems utilizing user-specific weights perform better compared to systems that assign the same set of weights to the multiple biometric traits of all users. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.