Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution

Poh, Norman, Windridge, David ORCID logoORCID:, Mottl, Vadim, Tatarchuk, Alexander and Eliseyev, Andrey (2010) Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security, 5 (3) . pp. 461-469. ISSN 1556-6013 [Article] (doi:10.1109/TIFS.2010.2053535)

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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 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 shows that the SVM-NPS approach achieves very good generalization performance compared to the sum rule fusion, especially with severe missing modalities.

Item Type: Article
Keywords (uncontrolled): biometrics (access control); pattern classification; sensor fusion;support vector machines; SVM training framework; kernel-based multimodal biometric fusion; missing modality; neutral point classification; neutral point substitution method; support vector machines; Authentication; Biometrics; Business; Government; Kernel; Security; Support vector machine classification; Support vector machines; Testing; Throughput; Biometric authentication; information fusion; missing features; multimodal biometrics; multiple classifiers system
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 15325
Notes on copyright: © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: David Windridge
Date Deposited: 27 Apr 2015 10:29
Last Modified: 30 Nov 2022 01:01

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