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Proceedings Paper

Kernel-based multimodal biometric verification using quality signals
Author(s): Julian Fierrez-Aguilar; Javier Ortega-Garcia; Joaquin Gonzalez-Rodriguez; Josef Bigun
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Paper Abstract

A novel kernel-based fusion strategy is presented. It is based on SVM classifiers, trade-off coefficients introduced in the standard SVM training and testing procedures, and quality measures of the input biometric signals. Experimental results on a prototype application based on voice and fingerprint traits are reported. The benefits of using the two modalities as compared to only using one of them are revealed. This is achieved by using a novel experimental procedure in which multi-modal verification performance tests are compared with multi-probe tests of the individual subsystems. Appropriate selection of the parameters of the proposed quality-based scheme leads to a quality-based fusion scheme outperforming the raw fusion strategy without considering quality signals. In particular, a relative improvement of 18% is obtained for small SVM training set size by using only fingerprint quality labels.

Paper Details

Date Published: 25 August 2004
PDF: 11 pages
Proc. SPIE 5404, Biometric Technology for Human Identification, (25 August 2004); doi: 10.1117/12.542800
Show Author Affiliations
Julian Fierrez-Aguilar, Univ. Politecnica de Madrid (Spain)
Javier Ortega-Garcia, Univ. Politecnica de Madrid (Spain)
Joaquin Gonzalez-Rodriguez, Univ. Politecnica de Madrid (Spain)
Josef Bigun, Halmstad Univ. (Sweden)

Published in SPIE Proceedings Vol. 5404:
Biometric Technology for Human Identification
Anil K. Jain; Nalini K. Ratha, Editor(s)

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