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Proceedings Paper

Multimodal biometric fusion using multiple-input correlation filter classifiers
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Paper Abstract

In this work we apply a computationally efficient, closed form design of a jointly optimized filter bank of correlation filter classifiers for biometric verification with the use of multiple biometrics from individuals. Advanced correlation filters have been used successfully for biometric classification, and have shown robustness in verifying faces, palmprints and fingerprints. In this study we address the issues of performing robust biometric verification when multiple biometrics from the same person are available at the moment of authentication; we implement biometric fusion by using a filter bank of correlation filter classifiers which are jointly optimized with each biometric, instead of designing separate independent correlation filter classifiers for each biometric and then fuse the resulting match scores. We present results using fingerprint and palmprint images from a data set of 40 people, showing a considerable advantage in verification performance producing a large margin of separation between the impostor and authentic match scores. The method proposed in this paper is a robust and secure method for authenticating an individual.

Paper Details

Date Published: 28 March 2005
PDF: 9 pages
Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); doi: 10.1117/12.604239
Show Author Affiliations
Pablo Hennings, Carnegie Mellon Univ. (United States)
Marios Savvides, Carnegie Mellon Univ. (United States)
B. V. K. Vijaya Kumar, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 5779:
Biometric Technology for Human Identification II
Anil K. Jain; Nalini K. Ratha, Editor(s)

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