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  4. On Learning Joint Multi-biometric Representations by Deep Fusion
 
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2019
Conference Paper
Titel

On Learning Joint Multi-biometric Representations by Deep Fusion

Abstract
Multi-biometrics combines different biometric sources to enhance recognition, template protection, and indexing performances. One of the main challenges here is the need for joint discriminant feature representation of multi-biometric data. This is typically achieved by feature-level fusion, imposing limitations on the combinations of biometric characteristics and algorithms. Including multiple imaging sources within deep-learning networks was generally limited to multiple sources of images of the same physical object, e.g., multi-spectral object detection. Previous biometrics works were limited to use deep-learning to extract representations of single biometric characteristics. In contrast to that, our work studies creating representations of one identity by sampling different physical objects, i.e. biometric characteristics. We adapted three architectures successfully to produce and discuss jointly learned representations for different levels of correlated data, modalities, instances, and presentations. Our evaluation proved the applicability of jointly learning biometric representations, especially when the data correlation is low.
Author(s)
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Dimitrov, Kristiyan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Braun, Andreas
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kuijper, Arjan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019
Project(s)
CRISP
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Conference on Biometrics - Theory, Applications and Systems (BTAS) 2019
Thumbnail Image
DOI
10.1109/BTAS46853.2019.9186011
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic- Smart Cit...

  • Lead Topic- Visual Co...

  • Research Line- Comput...

  • Research Line- Human ...

  • Biometrics

  • Multibiometrics

  • Information fusion

  • Face recognition

  • Iris recognition

  • CRISP

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