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Neighbor distance ratios and dynamic weighting in multi-biometric fusion

: Damer, Naser; Alkhatib, Wael; Braun, Andreas; Kuijper, Arjan


Alexandre, Luís A. (Ed.):
Pattern recognition and image analysis. 8th Iberian Conference, IbPRIA 2017 : Faro, Portugal, June 20-23, 2017; Proceedings
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10255)
ISBN: 978-3-319-58837-7 (Print)
ISBN: 978-3-319-58838-4 (Online)
ISBN: 3-319-58837-0
Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA) <8, 2017, Faro>
Fraunhofer IGD ()
multibiometrics; verification; biometric fusion; biometric identification system; 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 multibiometric 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.
In contrast, we focus on integrating the information imbedded in the relative relation between the comparison scores (within a 1:N comparison) in the biometric fusion process using a dynamic weighting scheme. This is performed by considering the neighbors distance ratio in the ranked comparisons to influence the dynamic weights of the fused scores. The evaluation was performed on the Biometric Scores Set BSSR1 database. The enhanced performance induced by including the neighbors distance ratio information within a dynamic weighting scheme in comparison to the baseline solution was shown by an average reduction of the equal error rate by more than 40% over the different test scenarios.