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: B.C., -1
Conference Title: International Conference on Multimodal Interfaces
Pub Type: Conferences
biometric classifiers, biometrics, face and fingerprint biometrics, multimodal biometric systems