P-score: Performance aligned normalization and an evaluation in score-level multi-biometric fusion

AutorDamer, Naser; Boutros, Fadi; Terhörst, Philipp; Braun, Andreas; Kuijper, Arjan
ArtConference Paper
AbstraktNormalization is an important step for different fusion, classification, and decision making applications. Previous normalization approaches considered bringing values from different sources into a common range or distribution characteristics. In this work we propose a new normalization approach that transfers values into a normalized space where their relative performance in binary decision making is aligned across their whole range. Multi-biometric verification is a typical problem where information from different sources are normalized and fused to make a binary decision and therefore a good platform to evaluate the proposed normalization. We conducted an evaluation on two publicly available databases and showed that the normalization solution we are proposing consistently outperformed state-of-the-art and best practice approaches, e.g. by reducing the false rejection rate at 0.01% false acceptance rate by 60- 75% compared to the widely used z-score normalization under the sum-rule fusion.
KonferenzEuropean Signal Processing Conference (EUSIPCO) <26, 2018, Roma>
ProjektBundes­ministerium für Bildung und Forschung BMBF/
ReferenzInstitute of Electrical and Electronics Engineers -IEEE-: 26th European Signal Processing Conference, EUSIPCO 2018: 3-7 September 2018, Roma, Italy. Piscataway, NJ: IEEE, 2018, pp. 1402-1406
SchlüsselISBN : 9789082797015