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Biometric fusion involves consolidating the output of two or more biometric classifiers in order to render a decision about the identity of an individual. The authors consider the problem of designing a fusion scheme when the number of training samples is limited, thereby undermining the use of a purely density-based scheme and the likelihood ratio test statistic; the output of multiple matchers report conflicting results; and the use of a single fusion rule may not be practical due to the diversity of scenarios encountered in the probe dataset.
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