Multimodal biometrics: issues in design and testing

Authors:
Robert Snelick;Mike Indovina;James Yen;Alan Mink
Affiliations:
National Institute of Standards and Technology, Gaithersburg, MD;National Institute of Standards and Technology, Gaithersburg, MD;National Institute of Standards and Technology, Gaithersburg, MD;National Institute of Standards and Technology, Gaithersburg, MD
Venue:
Proceedings of the 5th international conference on Multimodal interfaces
Year:
2003

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Abstract

Experimental studies show that multimodal biometric systems for small-scale populations perform better than single-mode biometric systems. We examine if such techniques scale to larger populations, introduce a methodology to test the performance of such systems, and assess the feasibility of using commercial off-the-shelf (COTS) products to construct deployable multimodal biometric systems. A key aspect of our approach is to leverage confidence level scores from preexisting single-mode data. An example presents a multimodal biometrics system analysis that explores various normalization and fusion techniques for face and fingerprint classifiers. This multimodal analysis uses a population of about 1000 subjects, a number ten-times larger than seen in any previously reported study. Experimental results combining face and fingerprint biometric classifiers reveal significant performance improvement over single-mode biometric systems.