Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
Article
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), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535
Type | Article |
---|---|
Title | Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution |
Authors | Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. |
Abstract | 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. |
Keywords | 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 |
Publisher | Institute of Electrical and Electronics Engineers |
Journal | IEEE Transactions on Information Forensics and Security |
ISSN | 1556-6013 |
Publication dates | |
Sep 2010 | |
Publication process dates | |
Deposited | 27 Apr 2015 |
Accepted | 03 May 2010 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | © 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TIFS.2010.2053535 |
Scopus EID | 2-s2.0-77955673246 |
Language | English |
https://repository.mdx.ac.uk/item/851w4
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