Neyman-Pearson biometric score fusion as an extension of the sum rule

Jens Peter Hube

doi:10.1117/12.720009

12 April 2007 Neyman-Pearson biometric score fusion as an extension of the sum rule

Jens Peter Hube

Proceedings Volume 6539, Biometric Technology for Human Identification IV; 65390M (2007) https://doi.org/10.1117/12.720009
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Jens Peter Hube "Neyman-Pearson biometric score fusion as an extension of the sum rule", Proc. SPIE 6539, Biometric Technology for Human Identification IV, 65390M (12 April 2007); https://doi.org/10.1117/12.720009

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