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Multimodal Biometrics: Issues in Design and Testing
Published
Author(s)
Robert D. Snelick, Michael D. Indovina, James H. Yen, Alan Mink
Abstract
The results of experimental studies on multimodal biometric systems for small-scale populations have shown better performance compared to 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 of a multimodal biometrics system analysis is presented that explores various normalization and fusion techniques for face and fingerprint classifiers. This multimodal analysis uses a population of about 1000 subjects, which is a population size ten-times larger than used in any previously reported study. Experimental results combining face and fingerprint biometric classifiers reveal significant performance improvement over single-mode systems.
Proceedings Title
International Conference on Multimodal Interfaces | 5th | | ACM
Snelick, R.
, Indovina, M.
, Yen, J.
and Mink, A.
(2003),
Multimodal Biometrics: Issues in Design and Testing, International Conference on Multimodal Interfaces | 5th | | ACM, B.C., -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=151577
(Accessed October 10, 2025)