Abstract
Based on the fusion of multiple biometric sources, Multibiometric systems can be expected to be more accurate due to the presence of multiple pieces of evidence. Multibiometric system design is a challenging problem because it is very difficult to choose the optimal fusion strategy. Score level fusion is the most commonly used approach in Multibiometric systems. The distribution of genuine and imposter scores are very important for score fusion of Multibiometric systems. FRR (False Reject Rate) and FAR (False Accept Rate) are two key parameters to cultivate the distribution of genuine and imposter scores. In this paper, we first present a model for Multibiometric fusion and then proposed a novel approach for score level fusion which is based on FAR and FRR. By this method, the match scores first are transformed into LL1s and then the sum rule is used to combine the LL1s of the scores. The experimental results show that the new fusion scheme is efficient for different Multibiometric systems.
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Li, Y., Yin, J., Long, J., Zhu, E. (2009). A Novel Method for Multibiometric Fusion Based on FAR and FRR. In: Torra, V., Narukawa, Y., Inuiguchi, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2009. Lecture Notes in Computer Science(), vol 5861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04820-3_18
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DOI: https://doi.org/10.1007/978-3-642-04820-3_18
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