Keywords
biometric fusion, support vector machine
Abstract
Existing learning-based multi-modal biometric fusion techniques typically employ a single static Support Vector Machine (SVM). This type of fusion improves the accuracy of biometric classification, but it also has serious limitations because it is based on the assumptions that the set of biometric classifiers to be fused is local, static, and complete. We present a novel multi-SVM approach to multi-modal biometric fusion that addresses the limitations of existing fusion techniques and show empirically that our approach retains good classification accuracy even when some of the biometric modalities are unavailable.
Original Publication Citation
Sabra Dinerstein, Jonathan Dinerstein and Dan Ventura, "Robust Multi-Modal Biometric Fusion via Multiple SVMs", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 153-1535, 27.
BYU ScholarsArchive Citation
Dinerstein, Jonathan; Dinerstein, Sabra; and Ventura, Dan A., "Robust Multi-Modal Biometric Fusion via Multiple SVMs" (2007). Faculty Publications. 944.
https://scholarsarchive.byu.edu/facpub/944
Document Type
Peer-Reviewed Article
Publication Date
2007-10-07
Permanent URL
http://hdl.lib.byu.edu/1877/2533
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/