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Multimodal Biometrics: Issues in Design and Testing



Robert D. Snelick, Michael D. Indovina, James H. Yen, Alan Mink


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
Conference Dates
November 5-7, 2003
Conference Location
Conference Title
International Conference on Multimodal Interfaces


biometric classifiers, biometrics, face and fingerprint biometrics, multimodal biometric systems


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], (Accessed April 24, 2024)
Created November 1, 2003, Updated February 19, 2017