We define the biometric performance invariance under strictly monotonic functions on match scores as normalization
symmetry. We use this symmetry to clarify the essential difference between the standard score-level fusion
approaches of sum rule and Neyman-Pearson. We then express Neyman-Pearson fusion assuming match scores
defined using false acceptance rates on a logarithmic scale. We show that by stating Neyman-Pearson in this
form, it reduces to sum rule fusion for ROC curves with logarithmic slope. We also introduce a one parameter
model of biometric performance and use it to express Neyman-Pearson fusion as a weighted sum rule.
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