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Trust the biometric mainstream: Multi-biometric fusion and score coherence

: Damer, Naser; Rhaibani, Chadi Izzou; Braun, Andreas; Kuijper, Arjan


European Association for Signal Processing -EURASIP-; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
25th European Signal Processing Conference, EUSIPCO 2017 : 27 August - 2 September 2017, Kos Island, Greece
Piscataway, NJ: IEEE, 2017
ISBN: 978-0-9928626-7-1
ISBN: 978-0-9928626-8-8
ISBN: 978-1-5386-0751-0
European Signal Processing Conference (EUSIPCO) <25, 2017, Kos>
Conference Paper
Fraunhofer IGD ()
multibiometrics; biometric fusion; information fusion; CRISP; Guiding Theme: Digitized Work; Guiding Theme: Smart City; Research Area: Computer vision (CV); Research Area: Human computer interaction (HCI)

Multi-biometrics aims at building more accurate unified biometric decisions based on the information provided by multiple biometric sources. Information fusion is used to optimize the process of creating this unified decision. In previous works dealing with score-level multi-biometric fusion, the scores of different biometric sources belonging to the comparison of interest are used to create the fused score. This is usually achieved by assigning static weights for the different biometric sources with more advanced solutions considering supplementary dynamic information like sample quality and neighbours distance ratio.
This work proposes embedding score coherence information in the fusion process. This is based on our assumption that a minority of biometric sources, which points out towards a different decision than the majority, might have faulty conclusions and should be given relatively smaller role in the final decision. The evaluation was performed on the BioSecure multimodal biometric database with different levels of simulated noise. The proposed solution incorporates, and was compared to, three baseline static weighting approaches. The enhanced performance induced by including the coherence information within a dynamic weighting scheme in comparison to the baseline solution was shown by the reduction of the equal error rate by 45% to 85% over the different test scenarios and proved to maintain high performance when dealing with noisy data.