Damer, NaserNaserDamerBoutros, FadiFadiBoutrosTerhörst, PhilippPhilippTerhörstBraun, AndreasAndreasBraunKuijper, ArjanArjanKuijper2022-03-142022-03-142018https://publica.fraunhofer.de/handle/publica/40261610.23919/EUSIPCO.2018.8553553Normalization 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.enLead Topic: Smart CityLead Topic: Visual Computing as a ServiceResearch Line: Computer vision (CV)Research Line: Human computer interaction (HCI)multibiometricsinformation fusionbiometric fusionbiometricCRISP006conference paper