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Fingerprint and iris multi-biometric data indexing and retrieval

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


Institute of Electrical and Electronics Engineers -IEEE-:
21st International Conference on Information Fusion, FUSION 2018 : 10-13 July 2018, Cambridge, United Kingdom
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-4330-3
ISBN: 978-0-9964527-6-2
ISBN: 978-0-9964527-7-9
International Conference on Information Fusion (FUSION) <21, 2018, Cambridge>
Bundesministerium für Bildung und Forschung BMBF
Fraunhofer IGD ()
Guiding Theme: Digitized Work; Guiding Theme: Smart City; Research Area: Computer vision (CV); Research Area: Human computer interaction (HCI); biometric; multibiometrics; indexing; CRISP

Indexing of multi-biometric data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue in multi-biometric databases focused on multi-instance indexing, mainly iris data. Few works addressed the indexing in multi-modal databases, with basic candidate list fusion solutions limited to joining face and fingerprint data. Iris and fingerprint are widely used in large-scale biometric systems where fast retrieval is a significant issue. This work proposes joint multi-biometric retrieval solution based on fingerprint and iris data. This solution is evaluated under eight different candidate list fusion approaches with variable complexity on a database of 10,000 reference and probe records of irises and fingerprints. Our proposed multi-biometric retrieval of fingerprint and iris data resulted in a reduction of the miss rate (1- hit rate) at 0.1%penetration rate by 93% compared to fingerprint indexing and88% compared to iris indexing.