Addressing Missing Values in Kernel-Based Multimodal Biometric Fusion Using Neutral Point Substitution
Addressing Missing Values in Kernel-Based Multimodal Biometric Fusion Using Neutral Point Substitution
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DOI：
10.1109/tifs.2010.2053535
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发表时间：
2010-09
影响因子：
6.8
通讯作者：
N. Poh;David Windridge;V. Mottl;A. Tatarchuk;A. Eliseyev
中科院分区：
文献类型：
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作者：
N. Poh;David Windridge;V. Mottl;A. Tatarchuk;A. Eliseyev
在多模式生物特征信息融合中，经常会遇到无法进行匹配的缺失模式。因此，在比赛分数级别，这意味着分数将丢失。我们使用支持向量机(SVMs)和中性点替换(NPS)方法来处理涉及缺失模式(SCORE)的多模式融合问题。该方法首先使用内核处理每个通道。当一个通道丢失时，在内核级别，丢失的通道被称为中性点的关于分类的无偏见的通道所替代。关键是，与传统的缺失数据替换方法不同，由于中性点隐含地并入到支持向量机训练框架中，因此可以省略对中性点的显式计算。基于公开可用的生物安全DS2多模式(SCORES)数据集的实验表明，与和规则融合相比，支持向量机-NPS方法具有很好的泛化性能，特别是在严重缺失模式的情况下。
In multimodal biometric information fusion, it is common to encounter missing modalities in which matching cannot be performed. As a result, at the match score level, this implies that scores will be missing. We address the multimodal fusion problem involving missing modalities (scores) using support vector machines (SVMs) with the neutral point substitution (NPS) method. The approach starts by processing each modality using a kernel. When a modality is missing, at the kernel level, the missing modality is substituted by one that is unbiased with regards to the classification, called a neutral point. Critically, unlike conventional missing-data substitution methods, explicit calculation of neutral points may be omitted by virtue of their implicit incorporation within the SVM training framework. Experiments based on the publicly available Biosecure DS2 multimodal (scores) data set show that the SVM-NPS approach achieves very good generalization performance compared to the sum rule fusion, especially with severe missing modalities.