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

: Damer, Naser; Boutros, Fadi; Terhörst, Philipp; Braun, Andreas; Kuijper, Arjan


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society; European Association for Speech, Signal and Image Processing -EURASIP-:
26th European Signal Processing Conference, EUSIPCO 2018 : 3-7 September 2018, Roma, Italy
Piscataway, NJ: IEEE, 2018
ISBN: 978-9-0827-9701-5
ISBN: 978-90-827970-0-8
ISBN: 978-1-5386-3736-4
ISBN: 978-90-827970-1-5
European Signal Processing Conference (EUSIPCO) <26, 2018, Roma>
Bundesministerium für Bildung und Forschung BMBF
Fraunhofer IGD ()
Lead Topic: Smart City; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); Research Line: Human computer interaction (HCI); multibiometrics; information fusion; biometric fusion; biometric; CRISP

Normalization 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.