Abstract
This paper investigates the application of statistical methods in performance analysis of multimodal biometric systems. It develops an efficient and systematic approach to evaluate system performance under the influence of errors. Based upon the proposed approach, 126 experiments are conducted with the BSSR1 dataset on typical fusion methods using different normalization techniques. Experiment results demonstrate that the Simple Sum fusion method yields the best overall performance when working with Min-Max normalization. More importantly, further examination of experimental results reveals the need for systematic analysis of system performance as the performance of some fusion methods may exhibit big variations when the level of errors changes, and some fusion methods may produce very good performance in some application though normally unacceptable in others.
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