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Proceedings Paper

Neyman-Pearson biometric score fusion as an extension of the sum rule
Author(s): Jens Peter Hube
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Paper 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.

Paper Details

Date Published: 12 April 2007
PDF: 9 pages
Proc. SPIE 6539, Biometric Technology for Human Identification IV, 65390M (12 April 2007); doi: 10.1117/12.720009
Show Author Affiliations
Jens Peter Hube, L-1 Identity Solutions (United States)


Published in SPIE Proceedings Vol. 6539:
Biometric Technology for Human Identification IV
Salil Prabhakar; Arun A. Ross, Editor(s)

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