Addressing Missing Values in Kernel-based Multimodal Biometric Fusion using Neutral Point Substitution
Poh, N, Windridge, D, Mottl, V, Tatarchuk, A and Eliseyev, A (2010) Addressing Missing Values in Kernel-based Multimodal Biometric Fusion using Neutral Point Substitution IEEE Transactions on Information Forensics and Security, 5 (3). 461 - 469. ISSN 1556-6013
Available under License : See the attached licence file.
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.
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|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Depositing User:||Symplectic Elements|
|Date Deposited:||17 Feb 2012 08:13|
|Last Modified:||23 Sep 2013 19:01|
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